diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..12952a0 Binary files /dev/null and b/.DS_Store differ diff --git a/.zcode/plans/plan-sess_cf390556-4ef3-4ce6-b91e-782aee1718f9.md b/.zcode/plans/plan-sess_cf390556-4ef3-4ce6-b91e-782aee1718f9.md new file mode 100644 index 0000000..f0e3c24 --- /dev/null +++ b/.zcode/plans/plan-sess_cf390556-4ef3-4ce6-b91e-782aee1718f9.md @@ -0,0 +1,87 @@ +# 分层强化学习（HRL）实施计划 — 2026 高教社杯 B 题第三、四问 + +## 0. 现状与复用决策 + +仓库已有生产级基建，**全部复用、不重写**： + +| 现有组件 | 位置 | 用途 | +|---|---|---| +| `SimulatorClient`（HTTP+JSON、幂等重试、截止时钟、JSONL 日志） | `src/mcm26b/protocol/` | 模拟器对接（prompt 第 1 步已完成） | +| 本地评测机（内存回环，一局秒级） | `referee/` + `harness.LocalSimTransport` | 本地简化环境内核 | +| 网格幸存集滤波（真源保留定理已证明） | `src/mcm26b/particles.py` | 每频道信念后验 | +| 凸候选集 + 五状态机 | `src/mcm26b/belief/` | 频道状态观测 | +| 信息增益选点、TSP 2-opt、六边形覆盖、清除证书、后备清除 | `src/mcm26b/planner/` | 候选生成器 + 证书审批兜底 + 启发式基线 | +| 单步 MaskablePPO 全套（特征/掩码/BC/训练/审计） | `src/mcm26b/rl/` + `scripts/` | 代码模式模板 + 端到端对照基线 | + +**改进方向**（来自 v2 审计文档已知局限）：单步 RL 只做选点、学不到全场调度；Q4 后备清除偏多、尾部退化（13778s→11797s，距 4000s 目标远）。HRL 高层正是补**跨频道全场调度**决策。 + +**已确认的三个决策**：① 代码集成到 `src/mcm26b/hrl/`（README 给出与 prompt 目录树的映射表）；② 低层为字面 8 方向 PPO + 直线控制器对照（诚实预期：部署时只提交终点，低层对虚拟时间增益≈0，价值在架构完整性与消融验证）；③ 本次跑通全流程 + 短程训练（1–2h），完整课程训练交付脚本由用户自行启动。 + +## 1. 新建文件 + +``` +src/mcm26b/hrl/ +├── __init__.py +├── constants.py # HRL 接口常量：N_CANDIDATES=96、观测维度，feature_version=3 +├── candidates.py # 高层候选生成：待清除源/信息增益探索点/覆盖验证点/Q4 扇区探测点 + 动作掩码 +├── features.py # 高层观测（20 频道×14 统计 + 全局标量 + 96×6 候选特征 ≈864 维）、低层观测 +├── policy.py # HighLevelPolicy/LowLevelPolicy 加载器（沿用 PointSelectionPolicy 元数据校验模式） +├── low_level_env.py # 低层训练环境：随机起点→子目标，Discrete(8)，逐步计费，奖励=−Δd−转向+到达 +├── hierarchical_env.py # 高层整场环境：10–16 源，动作=子目标槽位，内嵌低层执行器，域随机化+课程参数 +└── executor.py # 子目标执行器：低层 PPO 步进（本地计费）或直线控制器（可切换），终点提交模拟器 + +scripts/ +├── train_hrl_low.py # 低层：BC 克隆直线控制器热启动 → MaskablePPO 微调；直线对照评估 +├── train_hrl_high.py # 高层：4 阶段课程（--stage --resume --timesteps），SubprocVecEnv 并行 +├── pretrain_hrl_expert.py # 可选：从 BayesLoop 调度器克隆 (obs, 子目标) 对热启动高层 +├── tune_hrl.py # Optuna TPE（prompt §10 搜索空间，预算 15–20 trials，初值复用 bayes 已调参数） +├── benchmark_hrl.py # 配对消融：HRL vs bayes/directional/单步 PPO，dev(700-702)/val(91000-91005)/stress 固定场景 +└── ablate_hrl.py # 四项消融：无信息增益奖励/无动作掩码/无课程/无粒子滤波 + +src/mcm26b/planner/hrl_loop.py # HRLLoop 运行时闭环（Q3/Q4），保留证书审批与异常留痕 +src/mcm26b/cli.py # 新增 --strategy hrl / --q4-strategy hrl / --hrl-model +tests/test_hrl.py # 特征布局、掩码、环境形状、奖励恒等式、训练-部署观测一致性 +docs/hrl.md # 设计文档 + prompt 目录树映射表 +docs/paper/ 增补 # 算法流程图、伪代码、超参表、训练曲线 +``` + +## 2. 核心设计 + +**高层动作空间** Discrete(96) + 掩码：槽位按固定顺序填充（已定位待清除源 ≤16 → PF 驱动探索点 → 覆盖网格未验证点 → Q4 扇区探测点），空槽/已清除频道/接收盘外候选掩码 −∞。 + +**高层观测**（≈864 维，参照 v2 的 401 维布局模式）：20 频道 × 14 特征（六状态 one-hot、MEC μx/μy、σx/σy/ρ、log 幸存格数、期望信息增益、距离、频道号）+ 全局 8 维（位置、时间比例、已清除数、最近检测结果编码/示向度/距离）+ 96×6 候选特征。 + +**step 语义**：高层选子目标 → executor 步进（低层 8 方向，每步 ≤100m，本地按 Δd/5 计费；部署时仅终点调模拟器）→ 按子目标类型执行 /measure 或 /clear → belief+PF 更新 → 奖励。 + +**奖励**：`r = −Δ虚拟耗时/100 + 100·清除 + 5·新检测 + w_info·ΔH − 冗余惩罚`，主口径与目标函数（虚拟时间）一致；w_info 可消融。 + +**课程 4 阶段**（prompt §7.1）：1 源/5 频道/无噪 → 3 源/10 频道/±0.5° → 8 源/16 频道/±1° → 10–16 源/20 频道/±1°；每阶段迁移上阶段权重，Q4 从 Q3 权重微调（定向比例递增），域随机化贯穿（源位置/频道/r_eff/朝向/噪声种子）。 + +**运行时闭环 HRLLoop**：初始覆盖相位（快速发现信号）→ 主循环高层决策（证书达标自动 /clear，与 BayesLoop 同语义）→ 全清即 /exit。不触碰候选集更新与清除判定的守恒性（真源保留定理依赖），异常留痕兜底保留。 + +## 3. 实施顺序（对应 prompt §12） + +1. `constants.py` + `candidates.py` + `features.py`（候选/掩码/观测）+ 单元测试 +2. `low_level_env.py` + `train_hrl_low.py`：低层短程训练，验证收敛到近直线（报告与直线最优的差距） +3. `hierarchical_env.py` + `executor.py` + `hierarchical_env` 单元测试（奖励恒等式：回合回报 = f(虚拟时间、清除数)） +4. `train_hrl_high.py` + 课程 + 域随机化 + SubprocVecEnv；`pretrain_hrl_expert.py` BC 热启动 +5. 短程课程训练（每阶段 2–5 万步，总计 1–2h），产出训练曲线 +6. `hrl_loop.py` + CLI 接入，`--local-judge` 本地演练验证全清正确性 +7. `benchmark_hrl.py` 配对对比（沿用 benchmark_rl.py 的 all_valid 有效口径）+ `ablate_hrl.py` 四项消融 +8. `tune_hrl.py` 受限 Optuna；产出最佳超参表 +9. 本地 referee 演练 ×3/题，表 1 格式结果（案例编码/清除数/平均定位清除时间/程序运行时间），日志 JSONL 原样归档 `output/hrl/` +10. `docs/hrl.md`、README 更新、论文素材（流程图/伪代码/超参表/对比分析，含"为什么分层 PPO、为什么非端到端、为什么非多智能体"论证） + +## 4. 验收标准 + +- 全部 pytest 通过（现有 243 项 + 新增 HRL 测试） +- `benchmark_hrl.py` 输出 all_valid=true（真值全清、缺席无误判、零异常、正常 exit、计时复算一致） +- 短程训练下 Q4 HRL 相对 directional 有可测改进方向（对标单步 RL 的 −14.38%）；Q3 如实报告与 bayes 的差距（v2 审计口径：Q3 RL 无稳定增益） +- 所有实验固定种子、命令可复现；消融表含均值±方差（≥5 种子） +- 完整训练只需用户执行 `scripts/train_hrl_high.py --full` 级别的命令 + +## 5. 风险与对策 + +- **高层整场回合过长（100–200 决策步）不收敛** → γ=1 + 密集 Δcost 奖励 + BC 从 bayes 调度器热启动 +- **低层增益≈0** → 如实消融报告（直线对照差异≈0），论文定位为架构必要性验证而非性能来源 +- **Q3 可能无增益** → 与 v2 口径一致如实报告，主打 Q4 调度收益；两种策略可共存（HRL 不改默认策略，仅在胜出后由用户决定切换默认） diff --git a/README.md b/README.md index 08697f5..7c939fd 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,8 @@ - [问题三搜索与总耗时改进方案（含七点覆盖证明和自建消融）](docs/algo-3.md) - [问题3/4 强化学习选点（PPO + CUDA）](docs/algo-rl.md) - [强化学习审核、修复与独立验证（2026-09-11）](docs/强化学习适用性审核与改进-20260911.md) +- [HRL 优化方案与 Sol 执行指南（含当前缺口、命令和验收门槛）](docs/hrl-sol-execution.md) +- [HRL P0+P1 执行报告（本轮实际命令、smoke 与多轮本地测试）](docs/hrl-p1-execution-report.md) - [论文](docs/paper/main.tex) ## 项目结构 @@ -83,6 +85,15 @@ uv run mcm26b run --robot-id 2026001 --self-test --strategy rl # 问题3 RL uv run mcm26b run --robot-id 2026001 --self-test --problem 4 --q4-strategy rl # 问题4 RL 闭环 uv run python scripts/benchmark_rl.py --seeds 5 # RL vs 手写策略配对消融 uv run python scripts/verify_rl_practice.py output/rl/official-practice/q4/20260911-rl-bc-01 +uv run python scripts/benchmark_hrl.py --variants hrl_heuristic,bayes,directional \ + --manifest output/hrl/manifests/dev.json --output output/hrl/p1-heuristic +uv run python scripts/train_hrl_high.py --problem 3 --stage 1 --timesteps 2048 \ + --envs 1 --vec-env dummy --device mps --config-profile production \ + --reward-profile time --model output/hrl/p1-smoke/q3.zip --eval-episodes 2 +# 训练前端（本地回环 Web UI）：实时进程/进度/参数收敛曲线，详见 docs/training-ui.md +uv run python -m mcm26b.training_ui --ui-port 8036 # http://127.0.0.1:8036 +uv run mcm26b run --robot-id 2026001 --self-test --strategy hrl \ + --hrl-model output/hrl/p1-smoke/q3.zip --hrl-device mps # 自检默认：源数按题面 10~16 随机；实机预算 30 分钟（--real-budget-s 可改，仅自检生效）； # 找全后持续复扫未清除频道直到限时（--config 关闭 persistent_search 可回到"找全即退"） uv run mcm26b run --robot-id <队号> --log output/run/session.jsonl # 对接模拟器正式/演练 diff --git a/docs/hrl-p1-execution-report.md b/docs/hrl-p1-execution-report.md new file mode 100644 index 0000000..1be4011 --- /dev/null +++ b/docs/hrl-p1-execution-report.md @@ -0,0 +1,117 @@ +# HRL P0+P1 执行报告（本地自建评测机） + +日期：2026-09-11（本机工作区） +范围：按用户确认，仅完成 P0 正确性回归 + P1 最小可训练/可评测闭环； +P2 的 BC、完整课程长训、Optuna、正式消融与正式测试表留待下一阶段。 + +## 1. 本轮代码交付 + +| 文件 | 内容 | +| --- | --- | +| `scripts/benchmark_hrl.py` | 同一 manifest 真值场景复用；`hrl`/`hrl_heuristic`/`bayes`/`directional` 配对评测；写 `results.json`、`aggregate.json/csv`、每局 `summary.json` 与 `session.jsonl` | +| `scripts/train_hrl_high.py` | 高层 MaskablePPO 训练；`--problem/--stage/--envs/--vec-env/--device/--config-profile/--reward-profile/--model/--resume/--init/--eval-manifest`；模型元数据固化 feature/action/reward 版本；墙钟硬上限 | +| `src/mcm26b/hrl/constants.py` | 新增 `ACTION_LAYOUT_VERSION=1`、`REWARD_VERSION=1` | +| `src/mcm26b/hrl/policy.py` | 高层模型严格校验元数据；低层模型保持独立基础校验 | +| `src/mcm26b/cli.py` | 新增 `--strategy hrl`、`--q4-strategy hrl`、`--hrl-model`、`--hrl-low-model`、`--hrl-device`、`--trace-low` | +| `src/mcm26b/hrl/hierarchical_env.py` | 新增 `HRLReward.time_only()`：P0.4 建议的纯时间目标，去掉逐源清除/发现/熵减塑形 | +| `project/` | 严格目录树薄兼容层，转发真实实现，不复制算法 | +| `tests/test_hrl_p1.py` | manifest 往返、真值隔离、纯时间奖励、project re-export 回归 | + +## 2. 全量回归 + +```bash +.venv/bin/python -m pytest -q --tb=short +# 275 passed in 124.57s +``` + +## 3. P1 smoke 训练（本机 Apple MPS） + +```bash +.venv/bin/python scripts/train_hrl_high.py \ + --problem 3 --stage 1 --timesteps 2048 --seed 320001 \ + --envs 1 --vec-env dummy --device mps \ + --config-profile production --reward-profile time \ + --model output/hrl/p1-smoke-time/q3.zip \ + --eval-episodes 2 --rollout-steps 128 --batch-size 128 \ + --n-epochs 3 --max-wall-s 800 --verbose 0 +``` + +结果： + +- `actual_timesteps=2048`，墙钟 `260.069 s`； +- 保存后确定性评估 2/2 成功，`accounted=20`，无异常； +- 模型元数据含 `feature_version=4`、`action_layout_version=1`、 + `reward_version=1`、`reward_profile=time`、`model_sha256`。 + +模型重载已由以下两条路径验证；`--resume` 的 `--timesteps` 是增量步数 +（SB3 `reset_num_timesteps=False` 语义），元数据写 `timesteps_semantics="additional"`： + +```bash +.venv/bin/python -m mcm26b.cli run \ + --robot-id 2026001 --self-test --strategy hrl \ + --hrl-model output/hrl/p1-smoke-time/q3.zip --hrl-device mps \ + --sources 3 --seed 1 --summary-steps 5 + +python scripts/benchmark_hrl.py --variants hrl \ + --hrl-model output/hrl/p1-smoke-time/q3.zip --hrl-device mps \ + --problem 3 --scenes 1 --sources 3 --output output/hrl/p1-bench-hrl +``` + +## 4. 多轮本地配对测试 + +### Q3：3 个独立场景 × 3 个变体 + +```bash +python scripts/benchmark_hrl.py --variants hrl,hrl_heuristic,bayes \ + --hrl-model output/hrl/p1-smoke-time/q3.zip --hrl-device mps \ + --problem 3 --scenes 3 --sources 3 --start-seed 91000 \ + --output output/hrl/p1-multiround-q3 +``` + +| variant | episodes | valid_rate | cleared_mean | virtual_time_mean_s | virtual_time_std_s | program_run_time_mean_s | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | +| bayes | 3 | 1.0 | 3.0 | 2518.371 | 74.183 | 0.748 | +| hrl_heuristic | 3 | 1.0 | 3.0 | 2518.371 | 74.183 | 0.883 | +| hrl | 3 | 1.0 | 3.0 | 3731.380 | 120.536 | 1.853 | + +结论：P1 模型只在 stage 1 单源课程上训练 2048 步，可直接重载并在 3 源场景保持 3/3 有效全清； +但用时明显未优于 bayes/heuristic，符合本轮不做正式性能声明的边界。 + +### Q4：3 个独立混合源场景 × 2 个变体 + +```bash +python scripts/benchmark_hrl.py --variants hrl_heuristic,directional \ + --problem 4 --scenes 3 --sources 3 --directional 1 --start-seed 92000 \ + --output output/hrl/p1-multiround-q4 --force +``` + +| variant | episodes | valid_rate | cleared_mean | virtual_time_mean_s | virtual_time_std_s | program_run_time_mean_s | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | +| directional | 3 | 1.0 | 3.0 | 13442.997 | 263.902 | 1.063 | +| hrl_heuristic | 3 | 1.0 | 3.0 | 12292.198 | 344.827 | 6.284 | + +说明：Q4 高层 PPO 尚未训练；本轮只验证 Q4 的 `hrl_heuristic` 与 `directional` 闭环、真值隔离与计时审计。 + +## 5. P0 状态 + +- `--resume` 重载：16 步起步模型续训 32 增量步后 `actual_timesteps=48`， + 校验 `feature_version/action_layout_version/reward_version` 后保存； + +- 槽位唯一性：`test_hrl.py` 覆盖 channel 17–20 与 1–4 同时 clear/locate 不丢； +- 清除事件一次且仅一次：fallback 成功写 `_step_clears` 已有回归； +- 候选快照：`test_opportunity_measure_precedes_candidate_snapshot` 已覆盖； +- 奖励对齐：新增 `HRLReward.time_only()`，训练脚本默认 `--reward-profile time`； +- 终止/安全网：现有 `HierarchicalEnv` 回合测试继续通过； +- 训练/部署配置：`--config-profile production` 使用生产配置，`coarse-pretrain` + 显式映射到 fast 评分配置并写入模型元数据； +- 依赖守卫：全量测试 275 项在 dev+rl 环境通过。 + +## 6. 未完成 / P2 待办 + +- `scripts/pretrain_hrl_expert.py` 已建可 `--help` 的接口占位，专家轨迹 + BC 功能待实现； +- `scripts/ablate_hrl.py` 已建接口占位，无信息增益/无掩码/无课程/无粒子统计消融待实现； +- `scripts/tune_hrl.py` 已建接口占位，Optuna TPE 搜索待实现； +- Q3→Q4 迁移训练与配对实验； +- 完整 10–16 源课程训练、多个训练种子、冻结测试清单； +- 官方三次正式测试表：必须有真实官方案例码与导出日志后才能填写； + 本地 `referee` 结果只标 `local_self_test`。 diff --git a/docs/hrl-sol-execution.md b/docs/hrl-sol-execution.md new file mode 100644 index 0000000..e2d6c74 --- /dev/null +++ b/docs/hrl-sol-execution.md @@ -0,0 +1,267 @@ +# HRL 续作执行说明：交给 Sol 的工程任务书 + +适用日期：2026-09-11。工作目录：本仓库根目录。先检查当前代码与本轮测试结果，再按 P0 → P1 → P2 → P3 顺序执行。 + +目标是交付可运行、可审计的整场调度学习系统；训练收益由完整场景验证决定，不把训练脚本存在或局部奖励上升视为完成。 +本说明更新了原粘贴 Prompt 与 `.zcode/plans/plan-sess_cf390556-4ef3-4ce6-b91e-782aee1718f9.md` 中不符合现有协议或证据的部分。 + +## 1. 现在有什么，缺什么 + +| 组件 | 当前状态 | Sol 的操作 | +| --- | --- | --- | +| `protocol/`、`referee/`、`harness.py` | 已有客户端、独立评测机、内存回环与计时审计 | 复用，不另建模拟器客户端 | +| `belief/`、`particles.py`、`planner/` | 已有保守候选、网格幸存集、覆盖证明、证书清除与后备清除 | 保持证明前提；改调度时复用 | +| `rl/`、`train_rl.py`、现有 RL 检查点 | 已有单源选点 PPO/MaskablePPO 与完整对照 | 作为局部策略和对照，勿命名为端到端 PPO | +| `hrl/constants.py`、`scheduler.py`、`planner/hrl_loop.py` | 已有高层候选、特征、执行与闭环骨架；存在本轮需修复的 P0 项 | 先完成第 4 节门槛 | +| `hrl/hierarchical_env.py`、`curriculum.py` | 已有训练环境和课程；奖励、终止、训练配置仍需审核 | 在长训前修正，覆盖独立环境/部署一致性 | +| `hrl/low_level_env.py`、`executor.py`、`train_hrl_low.py` | 有八方向到达实验；训练入口尚有掩码等缺陷 | 作为可选离线实验，不能卡住主线 | +| `tests/test_hrl.py` | 有接口和启发式 episode 测试 | 增补行为验证，不能只检验公式与实现一致 | +| `train_hrl_high.py`、`pretrain_hrl_expert.py` | 尚未实现 | P1 编写最小可训练闭环，P2 再加入 BC | +| `benchmark_hrl.py`、`ablate_hrl.py`、`tune_hrl.py` | 尚未实现 | 先评测，再训练，再做消融，最后受限调参 | +| CLI 的 `hrl` 策略和 `--hrl-model` | 当前入口尚未接入 | P1 接入并检验 `--help` 与离线闭环 | +| `output/hrl/` 的高层模型、曲线、评测表 | 本次审阅未发现这些产物 | 不声称已有 HRL 模型或训练成绩 | + +现有证据：修订单步 RL 的 Q3 独立验证均值比 `bayes` 低 0.69%，区间跨 0；Q4 比 `directional` 低 14.38%，比旧 PPO 低 2.26%，后一比较区间跨 0。 +这些来自固定模型、本地 18 场/题的验证；新 Q4 的后备清除次数更多，边界压力性能有退化。详情见 `docs/强化学习适用性审核与改进-20260911.md`。 +没有证据证明 HRL 已优于上述基线，没有证据支持“Q4 迁移节约 80% 训练成本”或“全部合法场景小于 4000 秒”。 + +## 2. 必须纠正的原 Prompt 假设 + +1. `/measure` 与 `/clear` 提交终点，移动按两次实际动作位置间的直线距离除以 5 计费；没有朝向、转弯或八方向移动协议。 +2. 八方向策略仅在本地推演再强制补上终点，无法影响模拟器实际路径；不能把这段推演称为比赛收益来源。 +3. `/clear` 不切换接收机频道，目标频道与当前接收机频道应分别记录；`near` 是测量结果，清除仍需独立请求。 +4. 接收半径逐源未知，动作掩码只能使用观测与保守界，不能读真实半径、源位置、类型或朝向。 +5. Q3 的 `no_signal` 只能按已证明的保守半径规则删格；Q4 的 `no_signal` 可能由方向遮挡造成，不能直接删空间候选。 +6. Q4 联合位置/半径/朝向粒子是后续研究选项；普通二维粒子加“角度门控”并不会自动得到正确联合后验。 +7. 固定地点误差固定；同地重测不是新独立样本。保留读数舍入裕量，不收紧真实的 ±1° 误差界来追求表面收敛。 +8. 源数最多 16 不等于只有 16 个频道；所有按频道编号索引的动作必须覆盖 1–20。 +9. 连续无信号次数不能作为全场结束条件；覆盖参数不能随意调到破坏已证明的发现覆盖。 +10. “无信息增益”“无动作掩码”“无粒子滤波”必须精确定义；不能通过取消清除证书或真源保留约束制造消融。 +11. 原 Prompt 的 6 个状态与实现里的状态枚举不一致；以 `ChannelStatus` 为准，额外 TRACKING/LOCATED 只能定义为派生特征。 +12. 协议文档 §6 仍写持续搜索默认开启；当前 `config.py` 为 `persistent_search=False`，CLI 用 `--persistent` 显式开启。先同步文档，不恢复过时默认值。 + +## 3. 推荐架构与工程边界 + +采用“高层学习调度 + 低层局部选点 + 协议执行”的分层架构。高层学习先处理哪个频道、继续覆盖还是定位、何时使用后备清除。 +低层默认调用成熟几何选点；可切换现有 `PointSelectionPolicy` 学习局部检测点，形成第二个学习层。直线移动与设备动作继续由协议客户端执行。 +Q3 首版低层用 `bayes` 几何选点；Q4 先保留 `directional` 的遮挡处理，待接线一致性通过后对比旧/新局部 PPO。 + +```text +SimulatorClient.state + belief + particles + coverage ledger + ↓ 同一份候选快照和动作掩码 +高层 MaskablePPO：选择频道/覆盖站/清除任务 + ↓ +局部选点：geometry | 已有 v2 PointSelectionPolicy + ↓ +证书/预算/覆盖约束 + 实际 measure/clear 宏动作 + ↓ +accepted 请求与响应 → 时间、信念、清除与覆盖账本 +``` + +八方向 PPO 保留为 `low_level_env.py` 的离线附录实验；生产默认无需加载。若论文要求字面双 PPO，用高层调度 PPO + 局部选点 PPO 对应真正可影响成绩的两个层次。 +现有高层一个动作可以包含整站逐频道扫描或多次后备清除，因此是变长宏动作。记录每个宏动作的实际持续时间、请求数和中断原因。 +先采用 `gamma=1` 与实际时间成本，避免把“每宏动作折扣一次”误当成“每秒折扣一次”；只有明确改变目标才引入按持续时间折扣。 +第一版保留 120 槽位 MLP，不同时重写注意力网络或连续动作；需要多候选/频道时另升动作版本并独立比较。 + +建议新增的共享接口如下；这些是待实现契约，避免训练和 CLI 分别拼接策略。 + +```python +@dataclass(frozen=True) +class HRLPolicyConfig: + local_selector: str = "geometry" # geometry | rl + local_model_path: str | None = None + +def make_hrl_loop(client, config, *, problem: int, + high_policy=None, policy_config=HRLPolicyConfig()) -> HRLLoop: ... + +def validate_episode(summary: dict, events: list[dict], + truth_channels: set[int]) -> dict: ... # 仅评测模块 + +def run_episode(scene: dict, variant: dict, *, output_dir: Path) -> dict: ... +``` + +## 4. P0：修复正确性，未过门槛不得长训 + +以下表中的状态以本轮提交和测试报告为准；Sol 接手时先查看 diff，已完成的项只做回归，不重复实现。 + +| 项目 | 精确入口与问题 | 目标与验收 | +| --- | --- | --- | +| P0.1 槽位冲突 | `constants.py`；`scheduler.build_candidates/heuristic_action`：clear 频道 17–20 与 locate 频道 1–4 重叠 | 本轮优先修复；每类按 20 频道分配，构造同时就绪冲突场景验证任务均保留 | +| P0.2 清除事件遗漏 | `scheduler._hrl_execute_fallback` 成功未写 `_step_clears` | 本轮优先修复；证书/后备/宏动作内部清除均由相同成功事件口径计数，一次且仅一次 | +| P0.3 候选过期 | `HRLLoop._coverage_phase` 在构建候选后 `_measure_at_stop` 可改变信念 | 本轮优先修复；选动作前候选/特征/掩码属于同一个状态快照 | +| P0.4 奖励对齐 | `HierarchicalEnv.step/_reward_from/HRLReward` | 改默认奖励，成功/异常/安全网/截断均计入已经发生的真实时间 | +| P0.5 回合终止 | `HierarchicalEnv.step/reset/close` | 实际完成、失败终止与采样截断分开；收尾不丢 /exit，不把零观测用于有效截断 bootstrap | +| P0.6 训练部署配置 | `curriculum.training_config` 默认网格 29.3m，部署 14.65m | 首个可比较模型使用生产几何配置；粗网格只能显式快速预训练，后接完整保真微调与评测 | +| P0.7 无进展循环 | `scheduler` 的 locate/probe/fallback 掩码及预算检查 | 去重、已清除/无效证书、后备预算不足在动作前处理；合法动作不能无限零成本自循环 | +| P0.8 测试与依赖 | `tests/test_hrl.py` 导入守卫、`policy.py/scheduler.py` 顶层 numpy | 不安装 RL 组时基础 CLI 可导入；完整 RL 环境运行测试时不得意外全部 skip | + +建议槽位布局为 clear 0–19、locate 20–39、station 40–88、fallback 89–108、保留 109–119；所有槽位互斥且小于 120。 +槽位含义改变必须升 `FEATURE_VERSION`，即使仍是 1248 维/120 动作，也必须拒绝旧语义检查点。 +模型元数据应要求 `level/problem/algorithm/feature_version/action_layout_version/reward_version`，并记录模型 SHA、配置、依赖版本、代码版本。 +缺字段不能默认为兼容；Q3 权重初始化 Q4 必须走显式训练迁移参数，部署加载仍严格检查题号。 + +默认奖励先用：`r = -实际本步虚拟时间增量 / 100`；证明完成时加固定完成奖，未完成的真实终止加失败罚。 +失败惩罚按冻结的 episode 最大成本界设置并记录，不随意用不足以覆盖数千秒的 `-100`。 +相同全清场景里逐源常数奖可能只是常数偏移，但不同终止路径、事件漏记和失败场景会改变激励；故首版去掉新发现/熵减/每源清除附加奖。 +如需塑形，另做势函数差 `gamma * Phi(next_state) - Phi(state)`，真正终止态统一势函数值，并检验总和望远镜消去。 +如果优化题目平均时间，用整场时间作为训练目标时明确这是所选代理；不同源数的跨场景均值与 `T/N` 均值不等价，不能暗称完全一致。 +人工步数上限若仅为采样截断，返回可用的终末观测并允许 bootstrap；若定义为任务失败期限，应作为真实终止计罚。按该语义设计测试。 + +P0 验收至少覆盖以下行为，不只验证 reward 按代码相加： + +- 20 个频道的各类槽位唯一；频道 17–20 与 1–4 同时有 clear/locate 不丢任务。 +- 同一观测轨迹分别构建独立训练环境与部署闭环，比较候选、mask、特征、下一实际请求及状态。 +- 固定动作序列跨成功、失败、后备与安全网路径：reward 的时间分项总和等于 accepted 日志复算时间。 +- Q4 保证盘内/盘外无信号不误删真源；正读数重置遮挡序列；无效证书不能执行清除。 +- 10/16 源、频道含 20、边界源与预算中断均检查真实源集合、缺席集合、正常退出、异常与计时。 +- 评测端真值仅进 `validate_episode`，策略构建、候选、mask、观测与模型选择均不得读取真值。 + +## 5. P1：最小完整闭环，先评测再短训 + +先实现 `scripts/benchmark_hrl.py` 与共享审计函数，使 `HRLLoop(high_policy=None)` 可以与原 `bayes/directional` 同场景对比。 +这个启发式 HRL 骨架是重要对照：若差距来自任务候选、机会测量、Q4 保底或预算语义变化，先修或如实量化，不归因给神经网络。 +每次运行将 scene、误差种子、配置、summary、完整 JSONL/accepted 事件、时间复算和模型 SHA 写入新目录；目录已存在则拒绝覆盖。 +单场异常也写结果；聚合存在任一无效局时 `all_valid=false` 且脚本非零退出，不从平均值表中静默删除失败场景。 + +随后实现 `scripts/train_hrl_high.py`，第一版提供以下参数并通过 `--help` 验证： + +```text +--problem {3,4} --stage {1,2,3,4} --timesteps INT --seed INT +--envs INT --vec-env {dummy,subproc} --device {cpu,cuda,mps,auto} +--config-profile {production,coarse-pretrain} +--model PATH --resume PATH --eval-manifest PATH --verbose INT +--local-selector {geometry,rl} --local-model PATH +``` + +阶段 1 即保持 20 频道与固定观测维度，只改变布景复杂度；早期课程可减少源数，但完整训练必须覆盖 10/13/16 源与实际 ±1° 固定地点误差。 +Q4 完整课程同时覆盖少量/半数/大量定向源及近边界向外朝向；Q3→Q4 迁移与 Q4 随机初始化作为配对实验。 +禁止让策略知道课程场景真实源数或隐藏的占用频道列表；初始位置保持官方 `(0,0)`，除非任务被明确标为局部交接课程。 +先测 1/2/4 个环境的采样吞吐、内存和每次决策耗时，再选择 Dummy/Subproc；小 MLP 不假定 GPU 更快。 +Subproc 环境类内部提供 `action_masks()`；Windows/macOS 使用入口守卫及可序列化工厂。 +随机种子覆盖 Python/NumPy/Torch、PPO、每个环境、场景误差；每个 worker 使用独立 Monitor 文件。 +保存实际 `model.num_timesteps` 与请求步数，恢复时说明增量还是累计；续训加载正确算法/特征版本且不覆盖原模型。 +使用掩码评估接口，如 `MaskableEvalCallback`；同时在确定性推理中传入同源 mask。 + +CLI 加入 `--strategy hrl`、`--q4-strategy hrl`、`--hrl-model` 与局部策略选项，经 `make_hrl_loop` 统一构建；默认策略保持现状。 +显式指定缺失模型应报可操作错误，不能把启发式降级结果记成 HRL 模型成绩；启发式骨架单独命名 `hrl_heuristic`。 + +P1 门槛：启发式骨架全场审计通过；512–2048 步训练冒烟可保存、重载、确定性推理；CLI 内存回环至少 Q3/Q4 各一场通过。 +短训先用单个种子验证流程，不用这次模型的成功率作论文结论；完成后记录吞吐再定时间预算，不预设 1–2 小时足够。 + +## 6. P2：完整保真训练、独立评测与消融 + +实现 `pretrain_hrl_expert.py`：运行 `hrl_heuristic`，保存 `(episode_id, state, candidates, mask, action)`；按 episode 划分训练/验证。 +BC 与 PPO 的网络层、激活、归一化、mask 处理保持一致；复制后在真实样本上逐位比较 logits，critic 单独初始化。 +记录专家动作无法映射到候选的比率与原因；若大量回退到最近候选，先改专家映射，不用假专家标签继续训练。 +至少比较 BC+PPO 与随机初始化 PPO；BC 准确率单独报告，不能代替独立闭环用时与有效全清率。 + +冻结数据集分工： + +| 集合 | 用途与约束 | +| --- | --- | +| 已有 700–702 开发场景 | 查实现错误、探索配置与性能分解 | +| 已有 91000–91005 场景 | 历史回归；结果已经被审阅，不再宣称是本次全新盲测 | +| 新开发/选择清单 | 固定 JSON manifest，记录 seed、源数、定向组成、error_seed；用于训练选择和调参 | +| 新冻结最终测试清单 | 与训练、BC、调参及选择清单不重叠；模型冻结后运行，失败后修改则新开一轮测试 | +| 压力场景 | 圆周边界、1000m 半径、向外/切向定向、高频道、固定误差与预算边界；单独汇总 | + +同场景每个变体用相同位置、频道、半径、朝向和地点误差函数。每个模型至少跑 30 个完整场景，再按预算扩大；“30”是工程起点，不是统计充分性证明。 +最终用于稳定收益结论的主方法至少 5 个独立训练种子；场景种子与训练种子分开报告，不能用 5 个场景替代 5 次训练。 +Q3、Q4 分开统计，并按源数/定向占比分层；所有基线与候选使用相同停止、预算和计时口径。 + +| 指标 | 固定定义 | +| --- | --- | +| 有效全清 | 成功清除频道集合等于评测真值；缺席集合与真值不交；completed、正常 exit、无异常、计时一致 | +| `virtual_time_s` | 实际退出前的完整虚拟时间；主竞赛口径显式 `persistent_search=False` | +| `avg_locate_clear_time_s` | 每场 `T / 实际清除数`；有效全清主表再做逐场平均，另报源数分组 | +| `accounted_virtual_time_s` | 首次完成证明的时间；与最终时间并列，不悄悄用它替换正式计分 | +| 运行时间 | 每场实际墙钟、模型推理耗时分位数；本地内存回环时间不冒充官方程序运行时间 | +| 成本分解 | 移动秒、测量秒、换频秒、成功/失败 clear 秒、后备尝试、重复测量、总请求数 | +| 尾部与失败 | 中位数/P90/P95/最大值、有效全清率、失败明细；小样本尾分位注明估计局限 | + +平均 `T/N` 不等于 `平均 T / 平均 N`；若另用总体总时间/总源数，明确标为按源数加权口径。 +配对效应以同场景时间差为基础，报告均值相对变化、胜出场数、95% 配对 bootstrap 区间；相同场景家族共享种子时按家族重采样。 +多训练种子结果报告种子间分散程度；必要时对训练种子和场景做分层重采样，不能用单一场景区间代表训练稳定性。 + +最小比较矩阵：`bayes/directional`、旧局部 PPO、修订局部 MaskablePPO、`hrl_heuristic`、高层 PPO+几何、后续高层 PPO+局部 PPO。 +此矩阵先回答“改调度是否有用”和“增加局部学习是否有用”；既有单步 RL 不能命名为无几何的端到端基线。 +若仍要端到端 PPO，必须单独定义输入、实际动作、安全执行边界并训练，标记预算；它不是 P1 完成条件。 + +每项消融使用相同训练步数/场景预算/种子和评测集，单次只改变一个因素： + +- 调度：学习高层 vs `hrl_heuristic`，保持候选生成和局部选点一致。 +- 局部策略：同一高层与执行接口下，geometry vs 现有局部 PPO；报告是冻结替换还是联合重训。 +- 塑形：纯时间目标 vs 经过恒等式核验的势函数塑形；无信息增益奖励就是系数为 0。 +- 课程：相同总交互步数的课程训练 vs 从完整场景开始训练；Q4 迁移另列，不预设节约百分比。 +- 掩码：只取消重复/低效候选等策略性屏蔽；协议合法性、证书和预算硬守卫始终保留，标明实际取消范围。 +- 粒子特征：保留几何守恒内核，先将粒子统计输入和评分消融；若真的移除粒子模块，另实现保守几何替代并重新验证。 +- 八方向：只报告离线到达率/路径效率/推演耗时，以及实际协议终点相同时成绩相同的事实。 + +P2 推广门槛：所有最终与压力场景有效，Q4 相对最强已冻结基线的时间差区间上界小于 0；尾部容忍度先写入 manifest。 +建议初始尾部门槛为 P95 不退化超过 5%，这是可调整的工程标准，不是题设要求；任何调整都在重跑最终测试前完成。 +Q3 若未显示稳定收益，保留 `bayes` 默认并交付负结果。仅比手写基线快、却未优于现有 PPO，不能宣布替换默认模型。 + +## 7. P3：受限调参、论文与官方验证 + +只有 P2 主方法具备竞争力才实现并运行 `tune_hrl.py`。先搜索学习率、熵系数、网络宽度、GAE、合法候选排序参数。 +不搜索真实误差上限、光学清除半径、无信号停止次数或破坏覆盖证明的网格间距；确定性网格没有粒子重采样阈值可调。 +Optuna 只用开发/选择清单，失败配置先按有效性淘汰；预算与失败/剪枝逻辑写入结果。最终模型重新用固定配置多种子训练。 +文档、流程图与论文表明确：学习负责哪些决策、证明由哪些模块提供、本地生成分布未知是否对应官方分布。 +训练曲线、实际步数、配置、全部对照与失败明细均归档；不要只挑最好种子或去掉不利的边界场景。 +官方三次测试需要对应演练已经启动、真实案例码与官方导出日志；本地 referee 的三场不能填写成“三次正式测试”。 +不覆盖旧官方归档；官方结果与本地结果分表，不对不同官方案例直接计算改进百分比。 + +## 8. 命令：已经存在与待实现的严格区分 + +以下入口当前已经存在；依赖需先安装，命令失败要修复或如实报告，不能跳过错误声称通过： + +```bash +uv sync --group dev --group rl +uv run python -m pytest -q --tb=short +uv run python -m pytest -q tests/test_hrl.py --tb=short +uv run mcm26b run --help +uv run mcm26b run --robot-id 2026001 --self-test --strategy bayes +uv run mcm26b run --robot-id 2026001 --self-test --problem 4 --q4-strategy directional +uv run python scripts/train_rl.py --help +uv run python scripts/train_hrl_low.py --help +``` + +`train_hrl_low.py` 目前仅 `--help` 可用于检查入口，不把完整训练列为已可用： +其 `MaskablePPO.learn()` 需要环境掩码，当前 `LowLevelEnv` 没有 `action_masks()`；改普通 PPO 或提供全 True 掩码后再试。 +还需修 BC 按 episode 划分、统一随机种子、每 worker 独立 Monitor 文件、评估 seed 生效、实际路径长度与成功率分开统计。 +低层到达失败不能靠 executor 最后强制补终点伪装成策略到达；环境裁剪后的实际移动长度也不能简单记为 `steps*100m`。 + +下面是 P1/P2 实现后必须支持并实测的命令契约，当前尚不能直接运行： + +```bash +# P1：先验证不加载模型的共享调度闭环 +uv run python scripts/benchmark_hrl.py --variants hrl_heuristic,bayes,directional --manifest output/hrl/manifests/dev.json --output output/hrl/p1-heuristic +# P1：完整保真冒烟；模型目录必须是新目录 +uv run python scripts/train_hrl_high.py --problem 3 --stage 1 --timesteps 2048 --seed 320001 --envs 1 --vec-env dummy --device cpu --config-profile production --model output/hrl/p1-smoke/q3.zip --verbose 0 +# P1：CLI 明确加载高层模型 +uv run mcm26b run --robot-id 2026001 --self-test --strategy hrl --hrl-model output/hrl/p1-smoke/q3.zip +# P2：正式阶段训练；步数先依据采样吞吐与学习曲线再调整 +uv run python scripts/train_hrl_high.py --problem 4 --stage 4 --timesteps 60000 --seed 420001 --envs 2 --vec-env dummy --device cpu --config-profile production --model output/hrl/p2/q4-seed420001.zip --eval-manifest output/hrl/manifests/select.json --verbose 0 +# P2：冻结模型完整对照；variants 的模型位置来自同目录的配置清单 +uv run python scripts/benchmark_hrl.py --variants hrl,hrl_heuristic,legacy,revised,baseline --manifest output/hrl/manifests/final.json --output output/hrl/p2-final +``` + +命令使用的 manifests 也要由脚本生成、校验并归档；尚不存在的模型不能当作初始化输入。 +添加 `--full` 一键课程入口时，它应展开并记录每阶段命令/配置/检查点链，失败即停，可继续，不是忽略错误的 shell 包装。 +参考现有依赖配置：基础运行依赖仅 requests；RL 为可选组。固定环境版本并记录硬件，勿假定当前执行机就是旧报告中的 RTX 4060。 + +## 9. 可以直接粘贴给 Sol 的续作 Prompt + +```text +继续当前仓库的 HRL 工作。先读 docs/hrl-sol-execution.md、最新测试结果与 git diff，保留现有修改。 +已有客户端/referee/几何/PPO 都要复用；不要重写骨架，不把缺失脚本或 CLI 说成已完成。 +先完成 P0 尚未关闭的奖励、终止、完整保真配置、掩码和依赖问题；本轮已修的槽位/清除事件/候选过期只回归。 +采用高层 MaskablePPO 调度 + 几何/已有局部 PPO 选点 + 协议直线执行；八方向只作为可选离线实验。 +按 P1 先实现 benchmark_hrl 与真值隔离审计，再实现 train_hrl_high、模型元数据和 CLI，跑可重载的短训冒烟。 +P0/P1 没通过就修实现，不用加训练步数掩盖问题。通过后再开展 BC、完整保真课程、多训练种子、冻结场景配对评测。 +保持 Q3 bayes/Q4 现有默认策略；完整实验胜出前不覆盖默认模型。主指标包含有效全清、T、逐场 T/N、请求分解和尾部。 +每阶段交付具体文件、实际执行命令、测试结果、模型/日志路径与未完成项；不预设 Q4 80% 节约或 4000 秒保证。 +官方测试只有真实演练开始并获得案例码/导出日志才记正式结果，本地实验单独标注。 +``` + +接口参考：[MaskablePPO 官方文档](https://sb3-contrib.readthedocs.io/en/master/modules/ppo_mask.html)，[Gymnasium 终止与时间限制](https://gymnasium.farama.org/tutorials/gymnasium_basics/handling_time_limits/)。 diff --git a/docs/training-ui.md b/docs/training-ui.md new file mode 100644 index 0000000..cd3717d --- /dev/null +++ b/docs/training-ui.md @@ -0,0 +1,103 @@ +# 模型训练前端（training-ui） + +本地回环 Web UI，用于**实时观察一次 HRL 高层 PPO 训练**：子进程状态、步进进度、 +参数收敛曲线、逐回合记录与完整 stdout 日志。设计与评测前端（`referee/` WebUI） +同构——同一套暗色面板语言、同一个「只监听 127.0.0.1 + 静态白名单 + JSON 轮询 +API」模式——差别在于本页托管的是一个**训练子进程**而不是评测会话。 + +## 启动 + +```bash +uv run python -m mcm26b.training_ui --ui-port 8036 # 打开 http://127.0.0.1:8036 +uv run python scripts/train_hrl_ui.py --ui-port 8036 # 等价薄入口（支撑材料命令） +uv run python -m mcm26b.training_ui --print-schema # 只看配置契约，不起服务 +uv run python -m mcm26b.training_ui --print-preview # 只看离线示例快照 +``` + +`--no-browser` 阻止自动打开浏览器；Ctrl+C 退出时会先停止训练子进程。 + +无训练进程时，界面顶栏「离线预览」可渲染三组合成快照（上升 / 平台期 / KL 过猛）， +用于检查曲线、指标卡、时间线与日志着色——预览不启动任何进程、不写任何文件。 + +## 界面结构 + +| 区块 | 内容 | +| --- | --- | +| 顶栏 | 任务摘要、进程/进度/墙钟/设备/连接五个状态 chip、刷新·预览·停止 | +| 执行进度与进程 | 步进进度条、剩余时间与预计结束、PID/吞吐/退出码、PPO 阶段时间线、完整命令行 | +| 训练配置 | 由 `/api/train/schema` 生成的五组表单 + 四个预设，运行中锁定 | +| 参数收敛状态 | 判读 chip + 判据说明，每个指标一张卡片（最新值、增量、迷你趋势线、异常高亮） | +| 曲线 | 主指标（平均回合奖励）+ 可切换诊断量，悬停十字线读数、KL=0.05 等参考线 | +| 实时运行日志 | 关键字过滤、错误/进度 JSON 开关、自动跟随、最近 4000 行 | +| 逐回合记录 | Monitor CSV 的每回合奖励/长度/时刻 | + +## 数据来源与契约 + +三个端点都在 `src/mcm26b/training_ui/server.py`： + +| 端点 | 说明 | +| --- | --- | +| `GET /api/train/schema` | 字段/分组/预设/指标字典，**与 `TrainConfig` 同源**（`schema.py`） | +| `GET /api/train/state` | 快照；支持 `log_tail`、`metric_limit`、`after_metric` 游标与 `preview=1` | +| `POST /api/train/start` | 允许只提交部分字段（其余取默认值），校验失败返回 409 | +| `POST /api/train/stop` | 向独立进程组发送 SIGTERM | + +进度数据的两个来源，服务端都已解析： + +1. **progress JSON**（主来源）：`train_hrl_high.py --progress-json` 每若干环境步打印 + 一行 `{"event": "progress", "timesteps": ..., "ep_rew_mean": ...}`，字段最全； + 热身阶段间隔会自动缩小，短程 smoke 也能看到完整曲线。 +2. **SB3 verbose 表格**（补充来源）：解析 `| key | value |` 表格，用于补 + `clip_fraction`/`learning_rate` 等诊断量。表格按段分组缓存、等本表的 + `total_timesteps` 到位后统一打横轴刻度；与 JSON 同一步数的表格版本会被跳过， + 避免重复点。 + +`timesteps` 是整个前端的横轴：服务端不伪造刻度（缺刻度的表宁可丢弃，截断的表 +按末尾已知刻度兜底并在收尾提交），前端不再自行编号。 + +## 收敛判读口径 + +`summarize_convergence()` 只做**可解释的启发式判读**，不替代正式学习曲线分析： + +| 状态 | 触发条件 | +| --- | --- | +| 等待首个 rollout | 尚无任何指标 | +| 预热阶段 | 进度点 < 3，无法判断趋势 | +| KL 偏大，策略更新可能过猛 | 最近 `approx_kl` > 0.05（判定优先级最高） | +| 价值函数尚未拟合 | 最近 `explained_variance` < 0 | +| 奖励未形成上升趋势 | 最近窗口奖励斜率为负 | +| 已趋于平稳（疑似收敛） | 窗口对半位移 ≤ 逐点噪声、变异系数 ≤ 0.15、极差 ≤ 15% 量级 | +| 收敛趋势良好 | 其余（斜率正向） | + +平台判据特意用「对半窗口位移 / 逐点噪声」而不是单看斜率绝对值：慢速但持续的 +提升（斜率很小）不会被误判成收敛。所有判据数字都会写进 `notes`，便于人工复核。 + +## 安全边界 + +- 只监听 `127.0.0.1`；静态资源是固定白名单，不提供目录遍历。 +- 命令由服务端以**参数列表**构造，不经过 shell；字符串参数只拒绝控制字符。 +- 同一时刻只允许一个训练任务；停止用独立进程组 SIGTERM，避免残留子进程。 +- 产物路径限定在仓库根下解析，模型输出沿用训练脚本自身的 `--force`/`--resume` + 语义（两者互斥，由后端校验）。 + +## 测试 + +```bash +.venv/bin/python -m pytest tests/test_training_ui.py tests/test_training_ui_js.py -q +``` + +- `tests/test_training_ui.py`：配置契约同源、预设可直接提交、参数校验、SB3 表格 + 刻度打点、游标分页、`describe_process` 进度/ETA、示例快照与真实快照同键、 + 以及用**桩训练脚本真起子进程**的启动/轮询/停止/退出码全链路。 +- `tests/js/training_ui_harness.cjs`（经 `tests/test_training_ui_js.py` 调起）： + DOM 桩加载发布的 `charts.js`/`app.js` 原文，用服务端生成的真实快照驱动渲染， + 断言表单/指标卡/曲线/日志/逐回合表都拿到数据，覆盖增量游标三条分支与 + `classList`、HTML 转义、极端数值等边界。 + +## 已知取舍 + +- 进度点保留上限 5000（服务端 deque 与前端本地缓存同值），超长训练只保留最近一段。 +- 表格解析依赖 SB3 的段名（`rollout/`/`time/`/`train/`/`system/`）；SB3 若改表格 + 格式，退化路径是「只显示 progress JSON」，不会导致界面崩溃。 +- 收敛判读是启发式；正式结论仍需用 `scripts/benchmark_hrl.py` 的配对评测与 + 学习曲线人工复核。 diff --git a/logs/README.md b/logs/README.md new file mode 100644 index 0000000..9e2ea93 --- /dev/null +++ b/logs/README.md @@ -0,0 +1,4 @@ +# logs/ + +正式测试/演练导出的 JSONL 与汇总日志放置目录。本地 P1 训练/评测日志实际写入 +`output/hrl/...`；本目录用于最终支撑材料归档，不修改文件名。 diff --git a/models/README.md b/models/README.md new file mode 100644 index 0000000..11bc1a3 --- /dev/null +++ b/models/README.md @@ -0,0 +1,4 @@ +# models/ + +最终冻结模型归档目录。训练脚本默认把模型写到 `--model` 指定路径； +P1 smoke 产物见 `output/hrl/p1-smoke-time/`。正式提交前把冻结模型复制到本目录并保留同名 `.json` 元数据。 diff --git a/output/hrl/manifests/error-smoke.json b/output/hrl/manifests/error-smoke.json new file mode 100644 index 0000000..f0ba8dd --- /dev/null +++ b/output/hrl/manifests/error-smoke.json @@ -0,0 +1,22 @@ +{ + "version": 1, + "problem": 3, + "scenes": [ + { + "scene_id": "scene-91000", + "seed": 91000, + "error_seed": 101000, + "problem": 3, + "sources": [ + { + "channel": 2, + "x": -427.13940484705535, + "y": -435.9099799795024, + "r_eff_m": 1060.4283745320529, + "directional": false, + "heading_deg": 0.0 + } + ] + } + ] +} \ No newline at end of file diff --git a/output/hrl/manifests/p1-q3-3scene.json b/output/hrl/manifests/p1-q3-3scene.json new file mode 100644 index 0000000..55a5a01 --- /dev/null +++ b/output/hrl/manifests/p1-q3-3scene.json @@ -0,0 +1,102 @@ +{ + "version": 1, + "problem": 3, + "scenes": [ + { + "scene_id": "scene-91000", + "seed": 91000, + "error_seed": 101000, + "problem": 3, + "sources": [ + { + "channel": 2, + "x": 1033.4162061135676, + "y": 980.9643630943525, + "r_eff_m": 1215.9020299595381, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 4, + "x": 1141.42692617697, + "y": 1214.64798855971, + "r_eff_m": 1144.9030947728022, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 14, + "x": 347.6455643335022, + "y": -308.6492782212773, + "r_eff_m": 1009.7577536167166, + "directional": false, + "heading_deg": 0.0 + } + ] + }, + { + "scene_id": "scene-91001", + "seed": 91001, + "error_seed": 101001, + "problem": 3, + "sources": [ + { + "channel": 2, + "x": 300.79719117417613, + "y": 711.2381336919588, + "r_eff_m": 1058.3261487121645, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 7, + "x": -126.73999601866822, + "y": -1794.2598689126896, + "r_eff_m": 1479.257881274853, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 3, + "x": -265.3063953487043, + "y": -1439.1227521180567, + "r_eff_m": 1160.2914166503724, + "directional": false, + "heading_deg": 0.0 + } + ] + }, + { + "scene_id": "scene-91002", + "seed": 91002, + "error_seed": 101002, + "problem": 3, + "sources": [ + { + "channel": 8, + "x": 6.181681080754516, + "y": -1135.468342651061, + "r_eff_m": 1490.0726602976297, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 11, + "x": -119.08187269520755, + "y": 1193.9811245150677, + "r_eff_m": 1058.2068154150713, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 3, + "x": -1254.331183487268, + "y": -934.0819165716706, + "r_eff_m": 1103.0991445098284, + "directional": false, + "heading_deg": 0.0 + } + ] + } + ] +} \ No newline at end of file diff --git a/output/hrl/manifests/p1-q4-3scene.json b/output/hrl/manifests/p1-q4-3scene.json new file mode 100644 index 0000000..25f5cb8 --- /dev/null +++ b/output/hrl/manifests/p1-q4-3scene.json @@ -0,0 +1,102 @@ +{ + "version": 1, + "problem": 4, + "scenes": [ + { + "scene_id": "scene-92000", + "seed": 92000, + "error_seed": 102000, + "problem": 4, + "sources": [ + { + "channel": 1, + "x": -1249.7105888724432, + "y": 171.2539925355322, + "r_eff_m": 1417.4365352620682, + "directional": true, + "heading_deg": 228.36554147567867 + }, + { + "channel": 6, + "x": -441.0665980322449, + "y": -1434.387727576605, + "r_eff_m": 1414.5119300808599, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 16, + "x": -1252.5571063612522, + "y": -734.4519283065093, + "r_eff_m": 1440.6783372243983, + "directional": false, + "heading_deg": 0.0 + } + ] + }, + { + "scene_id": "scene-92001", + "seed": 92001, + "error_seed": 102001, + "problem": 4, + "sources": [ + { + "channel": 12, + "x": -927.9442782494863, + "y": 362.8725319816366, + "r_eff_m": 1307.193450885175, + "directional": true, + "heading_deg": 172.72392850568954 + }, + { + "channel": 7, + "x": -1241.6295486236775, + "y": -53.50549468070716, + "r_eff_m": 1251.771750543368, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 18, + "x": -572.1716487141558, + "y": -131.35607931264857, + "r_eff_m": 1106.5738050310383, + "directional": false, + "heading_deg": 0.0 + } + ] + }, + { + "scene_id": "scene-92002", + "seed": 92002, + "error_seed": 102002, + "problem": 4, + "sources": [ + { + "channel": 16, + "x": -797.6201997604195, + "y": -1471.5756774197087, + "r_eff_m": 1149.3584422062138, + "directional": true, + "heading_deg": 18.02567832322149 + }, + { + "channel": 1, + "x": 7.219181159934594, + "y": -469.03034303024674, + "r_eff_m": 1228.1527219103295, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 17, + "x": -749.1440991605593, + "y": 1016.7239271296709, + "r_eff_m": 1059.8639512254615, + "directional": false, + "heading_deg": 0.0 + } + ] + } + ] +} \ No newline at end of file diff --git a/output/hrl/manifests/smoke-hrl.json b/output/hrl/manifests/smoke-hrl.json new file mode 100644 index 0000000..0be59c4 --- /dev/null +++ b/output/hrl/manifests/smoke-hrl.json @@ -0,0 +1,38 @@ +{ + "version": 1, + "problem": 3, + "scenes": [ + { + "scene_id": "scene-91000", + "seed": 91000, + "error_seed": 101000, + "problem": 3, + "sources": [ + { + "channel": 2, + "x": 1033.4162061135676, + "y": 980.9643630943525, + "r_eff_m": 1215.9020299595381, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 4, + "x": 1141.42692617697, + "y": 1214.64798855971, + "r_eff_m": 1144.9030947728022, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 14, + "x": 347.6455643335022, + "y": -308.6492782212773, + "r_eff_m": 1009.7577536167166, + "directional": false, + "heading_deg": 0.0 + } + ] + } + ] +} \ No newline at end of file diff --git a/output/hrl/manifests/smoke-q3-bayes.json b/output/hrl/manifests/smoke-q3-bayes.json new file mode 100644 index 0000000..0be59c4 --- /dev/null +++ b/output/hrl/manifests/smoke-q3-bayes.json @@ -0,0 +1,38 @@ +{ + "version": 1, + "problem": 3, + "scenes": [ + { + "scene_id": "scene-91000", + "seed": 91000, + "error_seed": 101000, + "problem": 3, + "sources": [ + { + "channel": 2, + "x": 1033.4162061135676, + "y": 980.9643630943525, + "r_eff_m": 1215.9020299595381, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 4, + "x": 1141.42692617697, + "y": 1214.64798855971, + "r_eff_m": 1144.9030947728022, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 14, + "x": 347.6455643335022, + "y": -308.6492782212773, + "r_eff_m": 1009.7577536167166, + "directional": false, + "heading_deg": 0.0 + } + ] + } + ] +} \ No newline at end of file diff --git a/output/hrl/manifests/smoke-q3.json b/output/hrl/manifests/smoke-q3.json new file mode 100644 index 0000000..0be59c4 --- /dev/null +++ b/output/hrl/manifests/smoke-q3.json @@ -0,0 +1,38 @@ +{ + "version": 1, + "problem": 3, + "scenes": [ + { + "scene_id": "scene-91000", + "seed": 91000, + "error_seed": 101000, + "problem": 3, + "sources": [ + { + "channel": 2, + "x": 1033.4162061135676, + "y": 980.9643630943525, + "r_eff_m": 1215.9020299595381, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 4, + "x": 1141.42692617697, + "y": 1214.64798855971, + "r_eff_m": 1144.9030947728022, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 14, + "x": 347.6455643335022, + "y": -308.6492782212773, + "r_eff_m": 1009.7577536167166, + "directional": false, + "heading_deg": 0.0 + } + ] + } + ] +} \ No newline at end of file diff --git a/output/hrl/manifests/smoke-q4.json b/output/hrl/manifests/smoke-q4.json new file mode 100644 index 0000000..7410437 --- /dev/null +++ b/output/hrl/manifests/smoke-q4.json @@ -0,0 +1,38 @@ +{ + "version": 1, + "problem": 4, + "scenes": [ + { + "scene_id": "scene-91000", + "seed": 91000, + "error_seed": 101000, + "problem": 4, + "sources": [ + { + "channel": 2, + "x": 1033.4162061135676, + "y": 980.9643630943525, + "r_eff_m": 1215.9020299595381, + "directional": true, + "heading_deg": 20.58031672094824 + }, + { + "channel": 4, + "x": 1141.42692617697, + "y": 1214.64798855971, + "r_eff_m": 1144.9030947728022, + "directional": false, + "heading_deg": 0.0 + }, + { + "channel": 14, + "x": 347.6455643335022, + "y": -308.6492782212773, + "r_eff_m": 1009.7577536167166, + "directional": false, + "heading_deg": 0.0 + } + ] + } + ] +} \ No newline at end of file diff --git a/output/hrl/p1-bench-bayes/aggregate.csv b/output/hrl/p1-bench-bayes/aggregate.csv new file mode 100644 index 0000000..3ca2d4b --- /dev/null +++ b/output/hrl/p1-bench-bayes/aggregate.csv @@ -0,0 +1,2 @@ +scene_id,variant,problem,truth_count,cleared_count,virtual_time_s,avg_locate_clear_time_s,accounted_virtual_time_s,program_run_time_s,real_elapsed_total_s,total_requests,measures,clears,clear_success,completed,stop_reason,exit_reason,valid,cleared_matches_truth,no_source_declared_absent,truth_channels,declared_absent_truth,anomaly_count +scene-91000,bayes,3,3,3,2613.443,871.148,2613.443,0.832516,0.082,137,134,3,3,True,all_resolved,user_exit,True,True,True,"[2, 4, 14]",[],0 diff --git a/output/hrl/p1-bench-bayes/aggregate.json b/output/hrl/p1-bench-bayes/aggregate.json new file mode 100644 index 0000000..62cc78f --- /dev/null +++ b/output/hrl/p1-bench-bayes/aggregate.json @@ -0,0 +1,27 @@ +{ + "variants": { + "bayes": { + "episodes": 1, + "valid_episodes": 1, + "valid_rate": 1.0, + "success_rate": 1.0, + "cleared_count_mean": 3.0, + "cleared_count_std": 0.0, + "virtual_time_mean_s": 2613.443, + "virtual_time_std_s": 0.0, + "virtual_time_min_s": 2613.443, + "virtual_time_max_s": 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"hrl_executor_trace": [] +} \ No newline at end of file diff --git a/output/hrl/p1-error-smoke/aggregate.csv b/output/hrl/p1-error-smoke/aggregate.csv new file mode 100644 index 0000000..aa923ce --- /dev/null +++ b/output/hrl/p1-error-smoke/aggregate.csv @@ -0,0 +1,4 @@ +scene_id,variant,problem,truth_count,cleared_count,virtual_time_s,avg_locate_clear_time_s,accounted_virtual_time_s,program_run_time_s,real_elapsed_total_s,total_requests,measures,clears,clear_success,completed,stop_reason,exit_reason,valid,cleared_matches_truth,no_source_declared_absent,truth_channels,declared_absent_truth,anomaly_count,error +scene-91000,hrl,3,1,0,,,,,,,,,,False,,,False,False,False,[2],[],0,"RuntimeError: 未找到 HRL 模型 output/hrl/no-such-model.zip。请先训练： + uv sync --group rl --group dev + uv run python scripts/train_hrl_high.py --problem 3" diff --git a/output/hrl/p1-error-smoke/aggregate.json b/output/hrl/p1-error-smoke/aggregate.json new file mode 100644 index 0000000..3bbb914 --- /dev/null +++ b/output/hrl/p1-error-smoke/aggregate.json @@ -0,0 +1,27 @@ +{ + "variants": { + "hrl": { + "episodes": 1, + "valid_episodes": 0, + "valid_rate": 0.0, + "success_rate": 0.0, + "cleared_count_mean": 0.0, + "cleared_count_std": 0.0, + "virtual_time_mean_s": 0.0, + "virtual_time_std_s": 0.0, + "virtual_time_min_s": 0.0, + "virtual_time_max_s": 0.0, + "avg_locate_clear_time_mean_s": null, + "program_run_time_mean_s": null, + "total_requests_mean": 0.0 + } + }, + "all_valid": false, + "wall_seconds": 1.039, + "manifest": "output/hrl/manifests/error-smoke.json", + "output": "output/hrl/p1-error-smoke", + "variants_requested": [ + "hrl" + ], + "scenes": 1 +} \ No newline at end of file diff --git a/output/hrl/p1-error-smoke/manifest.json b/output/hrl/p1-error-smoke/manifest.json new file mode 100644 index 0000000..f0ba8dd --- /dev/null +++ b/output/hrl/p1-error-smoke/manifest.json @@ -0,0 +1,22 @@ +{ + "version": 1, + "problem": 3, + "scenes": [ + { + "scene_id": "scene-91000", + "seed": 91000, + "error_seed": 101000, + "problem": 3, + "sources": [ + { + "channel": 2, + "x": -427.13940484705535, + "y": -435.9099799795024, + "r_eff_m": 1060.4283745320529, + "directional": false, + "heading_deg": 0.0 + } + ] + } + ] +} \ No newline at end of file diff --git a/output/hrl/p1-error-smoke/results.json b/output/hrl/p1-error-smoke/results.json new file mode 100644 index 0000000..bf7f2a3 --- /dev/null +++ b/output/hrl/p1-error-smoke/results.json @@ -0,0 +1,30 @@ +[ + { + "scene_id": "scene-91000", + "variant": "hrl", + "problem": 3, + "truth_count": 1, + "cleared_count": 0, + "virtual_time_s": null, + "avg_locate_clear_time_s": null, + "accounted_virtual_time_s": null, + "program_run_time_s": null, + "real_elapsed_total_s": null, + "total_requests": null, + "measures": null, + "clears": null, + "clear_success": null, + "completed": false, + "stop_reason": null, + "exit_reason": null, + "valid": false, + "cleared_matches_truth": false, + "no_source_declared_absent": false, + 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a/output/hrl/p1-resume-smoke/high_curve_q3_env0.monitor.csv b/output/hrl/p1-resume-smoke/high_curve_q3_env0.monitor.csv new file mode 100644 index 0000000..daf3c86 --- /dev/null +++ b/output/hrl/p1-resume-smoke/high_curve_q3_env0.monitor.csv @@ -0,0 +1,5 @@ +#{"t_start": 1789128366.957925, "env_id": "None"} +r,l,t +-2.233926,9,15.084848 +-0.859928,9,16.243314 +-1.223741,10,17.348262 diff --git a/output/hrl/p1-resume-smoke/q3.json b/output/hrl/p1-resume-smoke/q3.json new file mode 100644 index 0000000..b57c4d2 --- /dev/null +++ b/output/hrl/p1-resume-smoke/q3.json @@ -0,0 +1,45 @@ +{ + "level": "high", + "problem": 3, + "stage": 1, + "stage_name": "s1-single", + "algorithm": "maskable_ppo", + "feature_version": 4, + "action_layout_version": 1, + "reward_version": 1, + "obs_dim": 1248, + "act_dim": 120, + "seed": 7, + "requested_timesteps": 32, + "actual_timesteps": 48, + "n_envs": 1, + "vec_env": "dummy", + "config_profile": "production", + "fidelity": "production", + "reward_profile": 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+-2.888544,9,243.136185 +22.743345,9,244.218176 +14.62276,9,245.304769 +19.107381,9,246.457128 +10.420714,9,247.484148 +22.493237,9,248.506425 +17.547339,9,249.575724 +-6.252943,9,250.649927 diff --git a/output/hrl/p1-smoke/q3.json b/output/hrl/p1-smoke/q3.json new file mode 100644 index 0000000..8dfc559 --- /dev/null +++ b/output/hrl/p1-smoke/q3.json @@ -0,0 +1,67 @@ +{ + "level": "high", + "problem": 3, + "stage": 1, + "stage_name": "s1-single", + "algorithm": "maskable_ppo", + "feature_version": 4, + "obs_dim": 1248, + "act_dim": 120, + "seed": 320001, + "requested_timesteps": 2048, + "actual_timesteps": 2048, + "n_envs": 1, + "vec_env": "dummy", + "config_profile": "production", + "fidelity": "production", + "local_selector": "geometry", + "local_model": null, + "device": "mps", + "wall_seconds": 250.817, + "max_wall_s": 800.0, + "created_unix_s": 1789127731.636131, + "python": "3.13.9", + "platform": "macOS-27.0-arm64-arm-64bit-Mach-O", + "torch": "2.11.0", + "numpy": "2.5.3", + "stable_baselines3": "2.9.0", + "sb3_contrib": "2.9.0", + "mp_actor": true, + "model_sha256": "5884cf4a249f8f8c93c4fefbd0d514d3eacceb90a2ffacf173c35c89f790b604", + "monitor_files": [ + "output/hrl/p1-smoke/high_curve_q3_env0.monitor.csv" + ], + "reward_note": "HierarchicalEnv 当前使用 HRLReward 的时序奖励；P0.4 纯时间口径在消融/训练配置中显式记录，不静默替换。", + "evaluation": { + "episodes": 2, + "successes": 2, + "success_rate": 1.0, + "rows": [ + { + "episode": 0, + "seed": 1320001, + "steps": 9, + "cleared": 1, + "accounted": 20, + "virtual_time_s": 2655.664582, + "stop_reason": null, + "anomalies": [], + "success": true + }, + { + "episode": 1, + "seed": 1320002, + "steps": 9, + "cleared": 1, + "accounted": 20, + "virtual_time_s": 3140.481152, + "stop_reason": null, + "anomalies": [], + "success": true + } + ] + }, + "eval_manifest": null, + "action_layout_version": 1, + "reward_version": 1 +} \ No newline at end of file diff --git a/output/hrl/p1-smoke/q3.zip b/output/hrl/p1-smoke/q3.zip new file mode 100644 index 0000000..9465ab0 Binary files /dev/null and b/output/hrl/p1-smoke/q3.zip differ diff --git a/output/hrl/p1-smoke/train.log b/output/hrl/p1-smoke/train.log new file mode 100644 index 0000000..8ac15d8 --- /dev/null +++ b/output/hrl/p1-smoke/train.log @@ -0,0 +1,65 @@ +{ + "level": "high", + "problem": 3, + "stage": 1, + "stage_name": "s1-single", + "algorithm": "maskable_ppo", + "feature_version": 4, + "obs_dim": 1248, + "act_dim": 120, + "seed": 320001, + "requested_timesteps": 2048, + "actual_timesteps": 2048, + "n_envs": 1, + "vec_env": "dummy", + "config_profile": "production", + "fidelity": "production", + "local_selector": "geometry", + "local_model": null, + "device": "mps", + "wall_seconds": 250.817, + "max_wall_s": 800.0, + "created_unix_s": 1789127731.636131, + "python": "3.13.9", + "platform": "macOS-27.0-arm64-arm-64bit-Mach-O", + "torch": "2.11.0", + "numpy": "2.5.3", + "stable_baselines3": "2.9.0", + "sb3_contrib": "2.9.0", + "mp_actor": true, + "model_sha256": "5884cf4a249f8f8c93c4fefbd0d514d3eacceb90a2ffacf173c35c89f790b604", + "monitor_files": [ + "output/hrl/p1-smoke/high_curve_q3_env0.monitor.csv" + ], + "reward_note": "HierarchicalEnv 当前使用 HRLReward 的时序奖励；P0.4 纯时间口径在消融/训练配置中显式记录，不静默替换。", + "evaluation": { + "episodes": 2, + "successes": 2, + "success_rate": 1.0, + "rows": [ + { + "episode": 0, + "seed": 1320001, + "steps": 9, + "cleared": 1, + "accounted": 20, + "virtual_time_s": 2655.664582, + "stop_reason": null, + "anomalies": [], + "success": true + }, + { + "episode": 1, + "seed": 1320002, + "steps": 9, + "cleared": 1, + "accounted": 20, + "virtual_time_s": 3140.481152, + "stop_reason": null, + "anomalies": [], + "success": true + } + ] + }, + "eval_manifest": null +} diff --git a/output/hrl/p1-subproc-smoke/high_curve_q3_env0.monitor.csv b/output/hrl/p1-subproc-smoke/high_curve_q3_env0.monitor.csv new file mode 100644 index 0000000..16cfc0d --- /dev/null +++ b/output/hrl/p1-subproc-smoke/high_curve_q3_env0.monitor.csv @@ -0,0 +1,2 @@ +#{"t_start": 1789128198.743371, "env_id": "None"} +r,l,t diff --git a/output/hrl/p1-subproc-smoke/high_curve_q3_env1.monitor.csv b/output/hrl/p1-subproc-smoke/high_curve_q3_env1.monitor.csv new file mode 100644 index 0000000..d465f61 --- /dev/null +++ b/output/hrl/p1-subproc-smoke/high_curve_q3_env1.monitor.csv @@ -0,0 +1,2 @@ +#{"t_start": 1789128198.7435038, "env_id": "None"} +r,l,t diff --git a/output/hrl/p1-subproc-smoke/q3.json b/output/hrl/p1-subproc-smoke/q3.json new file mode 100644 index 0000000..2bd6d4e --- /dev/null +++ b/output/hrl/p1-subproc-smoke/q3.json @@ -0,0 +1,46 @@ +{ + "level": "high", + "problem": 3, + "stage": 1, + "stage_name": "s1-single", + "algorithm": "maskable_ppo", + "feature_version": 4, + "action_layout_version": 1, + "reward_version": 1, + "obs_dim": 1248, + "act_dim": 120, + "seed": 42, + "requested_timesteps": 16, + "actual_timesteps": 16, + "n_envs": 2, + "vec_env": "subproc", + "config_profile": "production", + "fidelity": "production", + "reward_profile": "time", + "local_selector": "geometry", + "local_model": null, + "device": "cpu", + "wall_seconds": 1.172, + "max_wall_s": 180.0, + "created_unix_s": 1789128200.571202, + "python": "3.13.9", + "platform": "macOS-27.0-arm64-arm-64bit-Mach-O", + "torch": "2.11.0", + "numpy": "2.5.3", + "stable_baselines3": "2.9.0", + "sb3_contrib": "2.9.0", + "mp_actor": true, + "model_sha256": "1cb9c3f03f1365fa711a8033f925708a9ae3a772e6dc25d6a260ba969011d842", + "monitor_files": [ + "output/hrl/p1-subproc-smoke/high_curve_q3_env0.monitor.csv", + "output/hrl/p1-subproc-smoke/high_curve_q3_env1.monitor.csv" + ], + "reward_note": "reward_profile=time 使用 HRLReward.time_only（P0.4 纯时间口径）；reward_profile=prompt 使用原始 §5.2 塑形。模型元数据已固化口径。", + "evaluation": { + "episodes": 0, + "successes": 0, + "success_rate": null, + "rows": [] + }, + "eval_manifest": null +} \ No newline at end of file diff --git a/output/hrl/p1-subproc-smoke/q3.zip b/output/hrl/p1-subproc-smoke/q3.zip new file mode 100644 index 0000000..69039cd Binary files /dev/null and b/output/hrl/p1-subproc-smoke/q3.zip differ diff --git a/output/hrl/p1-tiny-time/high_curve_q3_env0.monitor.csv b/output/hrl/p1-tiny-time/high_curve_q3_env0.monitor.csv new file mode 100644 index 0000000..d592b78 --- /dev/null +++ b/output/hrl/p1-tiny-time/high_curve_q3_env0.monitor.csv @@ -0,0 +1,4 @@ +#{"t_start": 1789127784.936411, "env_id": "None"} +r,l,t +0.132297,9,6.497756 +-0.015601,9,15.666472 diff --git a/output/hrl/p1-tiny-time/q3.json b/output/hrl/p1-tiny-time/q3.json new file mode 100644 index 0000000..599474c --- /dev/null +++ b/output/hrl/p1-tiny-time/q3.json @@ -0,0 +1,45 @@ +{ + "level": "high", + "problem": 3, + "stage": 1, + "stage_name": "s1-single", + "algorithm": "maskable_ppo", + "feature_version": 4, + "action_layout_version": 1, + "reward_version": 1, + "obs_dim": 1248, + "act_dim": 120, + "seed": 2, + "requested_timesteps": 32, + "actual_timesteps": 32, + "n_envs": 1, + "vec_env": "dummy", + "config_profile": "production", + "fidelity": "production", + "reward_profile": "time", + "local_selector": "geometry", + "local_model": null, + "device": "mps", + "wall_seconds": 15.971, + "max_wall_s": 120.0, + "created_unix_s": 1789127801.8324401, + "python": "3.13.9", + "platform": "macOS-27.0-arm64-arm-64bit-Mach-O", + "torch": "2.11.0", + "numpy": "2.5.3", + "stable_baselines3": "2.9.0", + "sb3_contrib": "2.9.0", + "mp_actor": true, + "model_sha256": "ccd89a54a9735ff550fd5692e1375441dc668f9c5c2bb496193f3bf616f0263e", + "monitor_files": [ + "output/hrl/p1-tiny-time/high_curve_q3_env0.monitor.csv" + ], + "reward_note": "reward_profile=time 使用 HRLReward.time_only（P0.4 纯时间口径）；reward_profile=prompt 使用原始 §5.2 塑形。模型元数据已固化口径。", + "evaluation": { + "episodes": 0, + "successes": 0, + "success_rate": null, + "rows": [] + }, + "eval_manifest": null +} \ No newline at end of file diff --git a/output/hrl/p1-tiny-time/q3.zip b/output/hrl/p1-tiny-time/q3.zip new file mode 100644 index 0000000..33c9538 Binary files /dev/null and b/output/hrl/p1-tiny-time/q3.zip differ diff --git a/output/hrl/p1-tiny/high_curve_q3_env0.monitor.csv b/output/hrl/p1-tiny/high_curve_q3_env0.monitor.csv new file mode 100644 index 0000000..45a4ce0 --- /dev/null +++ b/output/hrl/p1-tiny/high_curve_q3_env0.monitor.csv @@ -0,0 +1,8 @@ +#{"t_start": 1789127417.344619, "env_id": "None"} +r,l,t +32.196516,9,7.461324 +18.339641,9,8.569881 +34.101856,9,9.805799 +17.998514,11,20.358327 +15.299512,9,21.480242 +7.023546,9,22.549618 diff --git a/output/hrl/p1-tiny/q3.json b/output/hrl/p1-tiny/q3.json new file mode 100644 index 0000000..71fa82b --- /dev/null +++ b/output/hrl/p1-tiny/q3.json @@ -0,0 +1,54 @@ +{ + "level": "high", + "problem": 3, + "stage": 1, + "stage_name": "s1-single", + "algorithm": "maskable_ppo", + "feature_version": 4, + "obs_dim": 1248, + "act_dim": 120, + "seed": 1, + "requested_timesteps": 64, + "actual_timesteps": 64, + "n_envs": 1, + "vec_env": "dummy", + "config_profile": "production", + "fidelity": "production", + "local_selector": "geometry", + "local_model": null, + "device": "mps", + "wall_seconds": 22.794, + "max_wall_s": 120.0, + "created_unix_s": 1789127441.216903, + "python": "3.13.9", + "platform": "macOS-27.0-arm64-arm-64bit-Mach-O", + "torch": "2.11.0", + "numpy": "2.5.3", + "stable_baselines3": "2.9.0", + "sb3_contrib": "2.9.0", + "mp_actor": true, + "model_sha256": "996214e6b8462b303f72612e9d9b7a2cc80192633f9d0c3a7538ebcf304953a3", + "monitor_files": [ + "output/hrl/p1-tiny/high_curve_q3_env0.monitor.csv" + ], + "reward_note": "HierarchicalEnv 当前使用 HRLReward 的时序奖励；P0.4 纯时间口径在消融/训练配置中显式记录，不静默替换。", + "evaluation": { + "episodes": 1, + "successes": 0, + "success_rate": 0.0, + "rows": [ + { + "episode": 0, + "seed": 1000001, + "steps": 10, + "cleared": 1, + "accounted": 20, + "virtual_time_s": 3396.988584, + "stop_reason": null, + "anomalies": [], + "success": false + } + ] + }, + "eval_manifest": null +} \ No newline at end of file diff --git a/output/hrl/p1-tiny/q3.zip b/output/hrl/p1-tiny/q3.zip new file mode 100644 index 0000000..8dcc1ad Binary files /dev/null and b/output/hrl/p1-tiny/q3.zip differ diff --git a/output/hrl/ui/q3-main/high_curve_q3_env0.monitor.csv b/output/hrl/ui/q3-main/high_curve_q3_env0.monitor.csv new file mode 100644 index 0000000..e51c36a --- /dev/null +++ b/output/hrl/ui/q3-main/high_curve_q3_env0.monitor.csv @@ -0,0 +1,25 @@ +#{"t_start": 1789131470.7009711, "env_id": "None"} +r,l,t +-60.243412,29,8.236581 +-65.267146,32,15.567565 +-53.923248,31,24.181583 +-57.823319,29,30.313022 +-55.722281,30,38.524787 +-62.017972,30,44.443847 +-52.308001,31,51.635652 +-37.736802,30,59.332661 +-57.684121,29,68.877521 +-49.747156,28,76.268043 +-31.272008,27,81.1902 +-42.897332,31,87.992509 +-41.999388,29,94.172355 +-60.390471,29,100.168653 +-40.385813,30,107.371549 +-58.558701,28,113.905643 +-36.835948,29,120.560329 +-68.009389,31,127.67378 +-50.828949,30,134.399637 +-63.363896,30,141.347622 +-55.639525,29,147.81495 +-51.690185,32,155.758553 +-67.5672,31,163.026905 diff --git a/output/hrl/ui/q3-main/high_curve_q3_env1.monitor.csv b/output/hrl/ui/q3-main/high_curve_q3_env1.monitor.csv new file mode 100644 index 0000000..d0bd20f --- /dev/null +++ b/output/hrl/ui/q3-main/high_curve_q3_env1.monitor.csv @@ -0,0 +1,26 @@ +#{"t_start": 1789131470.688181, "env_id": "None"} +r,l,t +-61.637993,31,8.769517 +-60.371996,32,16.243656 +-58.508714,32,25.051409 +-83.837987,31,31.92371 +-51.701488,27,39.014709 +-49.616988,29,44.734395 +-53.487167,28,51.280801 +-59.054566,30,58.866042 +-55.318422,29,68.103129 +-54.296222,27,75.296567 +-70.891994,30,81.203023 +-68.286791,31,88.005299 +-48.240685,27,93.724577 +-74.055682,34,100.842993 +-52.555094,29,107.926491 +-47.789415,29,114.909193 +-47.368462,27,120.750774 +-41.793479,24,126.706623 +-57.076295,29,133.113687 +-62.39618,30,139.884742 +-51.973489,31,146.567253 +-42.16498,27,152.889253 +-46.519735,27,159.261741 +-45.396348,32,167.411655 diff --git a/output/hrl/ui/q3-main/high_curve_q3_env2.monitor.csv b/output/hrl/ui/q3-main/high_curve_q3_env2.monitor.csv new file mode 100644 index 0000000..402db45 --- /dev/null +++ b/output/hrl/ui/q3-main/high_curve_q3_env2.monitor.csv @@ -0,0 +1,25 @@ +#{"t_start": 1789131470.68818, "env_id": "None"} +r,l,t +-38.047619,27,7.631166 +-71.045108,31,14.706047 +-54.363499,29,22.797949 +-50.439802,30,29.284364 +-58.618438,31,37.797351 +-56.356787,29,43.677286 +-41.780158,28,49.942356 +-47.128871,30,57.468642 +-62.01468,30,67.136945 +-39.587121,26,73.774396 +-74.957926,32,80.636922 +-70.884237,31,87.238802 +-60.32913,33,94.422801 +-63.092012,30,100.659158 +-47.823824,27,107.183024 +-48.261152,29,113.931499 +-43.84811,31,120.931277 +-43.783425,31,128.060228 +-47.702071,27,134.231646 +-45.766946,28,140.640961 +-47.23532,27,146.567487 +-42.933501,30,153.720642 +-42.198285,29,160.548366 diff --git a/output/hrl/ui/q3-main/high_curve_q3_env3.monitor.csv b/output/hrl/ui/q3-main/high_curve_q3_env3.monitor.csv new file mode 100644 index 0000000..447a4b7 --- /dev/null +++ b/output/hrl/ui/q3-main/high_curve_q3_env3.monitor.csv @@ -0,0 +1,26 @@ +#{"t_start": 1789131470.688184, "env_id": "None"} +r,l,t +-66.079351,26,7.4159 +-57.92718,29,13.859275 +-36.389154,29,22.023231 +-48.410966,28,28.089911 +-60.981411,29,35.897358 +-40.72769,29,42.515953 +-42.725504,30,48.892828 +-68.119688,32,56.80918 +-44.757822,32,66.930061 +-70.991446,29,74.402907 +-42.136481,28,80.179351 +-47.034031,26,85.999898 +-70.710437,29,92.083854 +-47.640061,29,98.172139 +-44.13243,29,105.007124 +-42.164911,27,110.571928 +-50.74407,29,118.204797 +-61.53904,28,124.653143 +-55.78786,29,131.189011 +-61.387389,31,137.864159 +-50.450755,29,143.970732 +-36.938923,30,151.508712 +-56.708806,30,158.27327 +-51.756569,28,164.709839 diff --git a/output/hrl/ui/stoptest/high_curve_q3_env0.monitor.csv b/output/hrl/ui/stoptest/high_curve_q3_env0.monitor.csv new file mode 100644 index 0000000..f0e08e4 --- /dev/null +++ b/output/hrl/ui/stoptest/high_curve_q3_env0.monitor.csv @@ -0,0 +1,26 @@ +#{"t_start": 1789131119.466002, "env_id": "None"} +r,l,t +-2.512273,9,1.159931 +-4.431984,9,1.636388 +-2.846184,9,2.159246 +-7.566042,9,2.61502 +-3.836022,10,3.083946 +-3.913308,10,3.568985 +-6.901949,9,4.051997 +-6.906865,9,4.56144 +-5.878078,9,5.08209 +-1.350351,9,5.573905 +-7.066036,10,6.06973 +-8.13182,9,6.5292 +-2.591276,9,7.003616 +-4.444299,11,7.479121 +-8.617643,9,7.938101 +-4.426097,9,8.403249 +1.857707,9,8.883565 +-6.041048,9,9.372791 +2.924646,10,9.896171 +0.20063,9,10.42233 +-1.51934,9,10.912036 +-6.211991,9,11.38844 +-0.398325,9,11.914142 +-5.423484,9,12.364392 diff --git a/output/hrl/ui/test/crash/high_curve_q3_env0.monitor.csv b/output/hrl/ui/test/crash/high_curve_q3_env0.monitor.csv new file mode 100644 index 0000000..86b7af8 --- /dev/null +++ b/output/hrl/ui/test/crash/high_curve_q3_env0.monitor.csv @@ -0,0 +1,5 @@ +#{"t_start": 0.0} +r,l,t +0.000,10,0.000 +1.000,11,5.000 +2.000,12,10.000 diff --git a/output/hrl/ui/test/cursor/high_curve_q3_env0.monitor.csv b/output/hrl/ui/test/cursor/high_curve_q3_env0.monitor.csv new file mode 100644 index 0000000..86b7af8 --- /dev/null +++ b/output/hrl/ui/test/cursor/high_curve_q3_env0.monitor.csv @@ -0,0 +1,5 @@ +#{"t_start": 0.0} +r,l,t +0.000,10,0.000 +1.000,11,5.000 +2.000,12,10.000 diff --git a/output/hrl/ui/test/http/high_curve_q3_env0.monitor.csv b/output/hrl/ui/test/http/high_curve_q3_env0.monitor.csv new file mode 100644 index 0000000..86b7af8 --- /dev/null +++ b/output/hrl/ui/test/http/high_curve_q3_env0.monitor.csv @@ -0,0 +1,5 @@ +#{"t_start": 0.0} +r,l,t +0.000,10,0.000 +1.000,11,5.000 +2.000,12,10.000 diff --git a/output/hrl/ui/test/live/high_curve_q3_env0.monitor.csv b/output/hrl/ui/test/live/high_curve_q3_env0.monitor.csv new file mode 100644 index 0000000..86b7af8 --- /dev/null +++ b/output/hrl/ui/test/live/high_curve_q3_env0.monitor.csv @@ -0,0 +1,5 @@ +#{"t_start": 0.0} +r,l,t +0.000,10,0.000 +1.000,11,5.000 +2.000,12,10.000 diff --git a/output/hrl/ui/test/truncated/high_curve_q3_env0.monitor.csv b/output/hrl/ui/test/truncated/high_curve_q3_env0.monitor.csv new file mode 100644 index 0000000..86b7af8 --- /dev/null +++ b/output/hrl/ui/test/truncated/high_curve_q3_env0.monitor.csv @@ -0,0 +1,5 @@ +#{"t_start": 0.0} +r,l,t +0.000,10,0.000 +1.000,11,5.000 +2.000,12,10.000 diff --git a/project/README.md b/project/README.md new file mode 100644 index 0000000..621437f --- /dev/null +++ b/project/README.md @@ -0,0 +1,29 @@ +# project/ 薄兼容层 + +题目 Prompt 要求 `project/` 目录树；本仓库的真实算法保持唯一一份， +位于 `src/mcm26b`、`scripts/`。本目录只做 re-export/转发，不复制实现。 + +| Prompt 路径 | 真实实现 | +|---|---| +| `env/simulator_client.py` | `src/mcm26b/protocol/client.py` | +| `env/local_env.py` | `src/mcm26b/hrl/hierarchical_env.py`, `low_level_env.py` | +| `env/particle_filter.py` | `src/mcm26b/particles.py` | +| `policies/high_level_ppo.py` | `src/mcm26b/hrl/policy.py` | +| `policies/low_level_ppo.py` | `src/mcm26b/hrl/policy.py`, `low_level_env.py` | +| `train/curriculum.py` | `src/mcm26b/hrl/curriculum.py` | +| `train/domain_randomization.py` | `src/mcm26b/hrl/curriculum.py` + 配置包装 | +| `eval/evaluate.py` | `scripts/benchmark_hrl.py` | +| `eval/ablation.py` | `scripts/ablate_hrl.py`（P2） | +| `baselines/heuristic.py` | `src/mcm26b/planner/hrl_loop.py` 启发式降级 | +| `baselines/end_to_end_ppo.py` | 显式未实现；单源局部 PPO 不冒充 E2E | + +使用示例： + +```python +from project.env.local_env import make_local_env +env = make_local_env(problem=3, stage=1, seed=7) +obs, info = env.reset(seed=7) +``` + +P1 当前交付：`benchmark_hrl.py`、`train_hrl_high.py`、CLI `--strategy hrl`、 +`--q4-strategy hrl`。P2 的 BC/Optuna/消融在 smoke 通过后另开。 diff --git a/project/__init__.py b/project/__init__.py new file mode 100644 index 0000000..ac2fbef --- /dev/null +++ b/project/__init__.py @@ -0,0 +1,4 @@ +"""题目 Prompt 所需目录树的薄兼容层。 + +真实实现仍位于 ``src/mcm26b``；本包只做 re-export / CLI 转发，避免两份算法逻辑。 +""" diff --git a/project/baselines/__init__.py b/project/baselines/__init__.py new file mode 100644 index 0000000..93ed785 --- /dev/null +++ b/project/baselines/__init__.py @@ -0,0 +1,3 @@ +from .heuristic import HeuristicHRL, run_heuristic + +__all__ = ["HeuristicHRL", "run_heuristic"] diff --git a/project/baselines/end_to_end_ppo.py b/project/baselines/end_to_end_ppo.py new file mode 100644 index 0000000..e151f91 --- /dev/null +++ b/project/baselines/end_to_end_ppo.py @@ -0,0 +1,16 @@ +"""端到端 PPO 基线占位。 + +按本轮确认：当前只交付局部 PPO 基线；raw-observation/raw-action 的真正 +端到端 PPO 不在 P0+P1 范围内，因此显式标记未实现，避免把单源局部选点 +PPO 误称为端到端 baseline。 +""" +from __future__ import annotations + + +def train_end_to_end_ppo(*args, **kwargs): + raise NotImplementedError( + "本轮只交付局部 PPO 基线；真正的端到端 PPO 基线标记为未实现，" + "需单独立项定义原始观测、动作与安全执行边界。") + + +__all__ = ["train_end_to_end_ppo"] diff --git a/project/baselines/heuristic.py b/project/baselines/heuristic.py new file mode 100644 index 0000000..05d1922 --- /dev/null +++ b/project/baselines/heuristic.py @@ -0,0 +1,16 @@ +"""兼容入口：信息增益/TSP/覆盖规则 + 高层启发式调度。""" +from mcm26b.planner.bayes_loop import BayesLoop +from mcm26b.planner.hrl_loop import HRLLoop + + +def HeuristicHRL(*args, **kwargs): + """返回无模型的高层 HRL 闭环（heuristic_action 降级路径）。""" + kwargs.setdefault("high_policy", None) + return HRLLoop(*args, **kwargs) + + +def run_heuristic(*args, **kwargs): + return HeuristicHRL(*args, **kwargs).run() + + +__all__ = ["HeuristicHRL", "run_heuristic", "BayesLoop", "HRLLoop"] diff --git a/project/env/__init__.py b/project/env/__init__.py new file mode 100644 index 0000000..502254f --- /dev/null +++ b/project/env/__init__.py @@ -0,0 +1,6 @@ +from .simulator_client import SimulatorClient +from .local_env import HierarchicalEnv, LowLevelEnv, HRLReward, make_local_env +from .particle_filter import ChannelParticles + +__all__ = ["SimulatorClient", "HierarchicalEnv", "LowLevelEnv", "HRLReward", + "make_local_env", "ChannelParticles"] diff --git a/project/env/local_env.py b/project/env/local_env.py new file mode 100644 index 0000000..d5ff04b --- /dev/null +++ b/project/env/local_env.py @@ -0,0 +1,22 @@ +"""兼容入口：本地简化训练环境。 + +HierarchicalEnv 是高层宏动作环境；LowLevelEnv 是独立八方向导航环境。 +本文件不复制实现，只转发 mcm26b.hrl 下的真实类。 +""" +from mcm26b.hrl.hierarchical_env import HRLReward, HierarchicalEnv +from mcm26b.hrl.low_level_env import LowLevelEnv + +__all__ = ["HierarchicalEnv", "LowLevelEnv", "HRLReward", "make_local_env"] + + +def make_local_env(*, problem: int = 3, stage: int = 4, seed: int | None = None, + fidelity: str = "production"): + """按题目 Prompt 的四阶段编号创建训练环境。""" + from mcm26b.hrl.curriculum import STAGES + if not 1 <= int(stage) <= len(STAGES): + raise ValueError(f"stage 必须在 1..{len(STAGES)}") + env = HierarchicalEnv(problem=problem, stage=STAGES[int(stage) - 1], + fidelity=fidelity) + if seed is not None: + env.reset(seed=seed) + return env diff --git a/project/env/particle_filter.py b/project/env/particle_filter.py new file mode 100644 index 0000000..f9c6d9e --- /dev/null +++ b/project/env/particle_filter.py @@ -0,0 +1,4 @@ +"""兼容入口：逐频道网格粒子/幸存集。""" +from mcm26b.particles import ChannelParticles, angular_gap + +__all__ = ["ChannelParticles", "angular_gap"] diff --git a/project/env/simulator_client.py b/project/env/simulator_client.py new file mode 100644 index 0000000..43c1e56 --- /dev/null +++ b/project/env/simulator_client.py @@ -0,0 +1,8 @@ +"""兼容入口：真实类见 mcm26b.protocol.client。""" +from mcm26b.protocol.client import (ActionRejected, ClearOutcome, DeadlineExceeded, + ExitInfo, MeasureOutcome, ProtocolError, + ProtocolViolation, SimulatorClient) + +__all__ = ["SimulatorClient", "MeasureOutcome", "ClearOutcome", "ExitInfo", + "ProtocolError", "ActionRejected", "ProtocolViolation", + "DeadlineExceeded"] diff --git a/project/eval/__init__.py b/project/eval/__init__.py new file mode 100644 index 0000000..1fd6c71 --- /dev/null +++ b/project/eval/__init__.py @@ -0,0 +1,4 @@ +from .evaluate import run_benchmark +from .ablation import run_ablation + +__all__ = ["run_benchmark", "run_ablation"] diff --git a/project/eval/ablation.py b/project/eval/ablation.py new file mode 100644 index 0000000..a4696ce --- /dev/null +++ b/project/eval/ablation.py @@ -0,0 +1,20 @@ +"""兼容入口：调用 scripts/ablate_hrl.py（P2 后启用）。""" +from __future__ import annotations + +import importlib.util +from pathlib import Path + + +def run_ablation(argv=None) -> int: + path = Path(__file__).resolve().parents[2] / "scripts" / "ablate_hrl.py" + if not path.exists(): + raise NotImplementedError( + "scripts/ablate_hrl.py 尚未实现；P1 阶段只保证 benchmark/training/CLI。") + spec = importlib.util.spec_from_file_location("ablate_hrl_compat", path) + module = importlib.util.module_from_spec(spec) + assert spec.loader is not None + spec.loader.exec_module(module) + return int(module.main(argv)) + + +__all__ = ["run_ablation"] diff --git a/project/eval/evaluate.py b/project/eval/evaluate.py new file mode 100644 index 0000000..8eb7e21 --- /dev/null +++ b/project/eval/evaluate.py @@ -0,0 +1,17 @@ +"""兼容入口：调用 scripts/benchmark_hrl.py。""" +from __future__ import annotations + +import importlib.util +from pathlib import Path + + +def run_benchmark(argv=None) -> int: + path = Path(__file__).resolve().parents[2] / "scripts" / "benchmark_hrl.py" + spec = importlib.util.spec_from_file_location("benchmark_hrl_compat", path) + module = importlib.util.module_from_spec(spec) + assert spec.loader is not None + spec.loader.exec_module(module) + return int(module.main(argv)) + + +__all__ = ["run_benchmark"] diff --git a/project/policies/__init__.py b/project/policies/__init__.py new file mode 100644 index 0000000..a52064f --- /dev/null +++ b/project/policies/__init__.py @@ -0,0 +1,5 @@ +from .high_level_ppo import HighLevelPolicy, load_high_policy +from .low_level_ppo import LowLevelPolicy, load_low_policy + +__all__ = ["HighLevelPolicy", "load_high_policy", + "LowLevelPolicy", "load_low_policy"] diff --git a/project/policies/high_level_ppo.py b/project/policies/high_level_ppo.py new file mode 100644 index 0000000..4dd4407 --- /dev/null +++ b/project/policies/high_level_ppo.py @@ -0,0 +1,10 @@ +"""兼容入口：高层 MaskablePPO 推理策略。""" +from mcm26b.hrl.policy import HighLevelPolicy + + +def load_high_policy(model_path: str, *, device: str = "auto", + problem: int | None = None) -> HighLevelPolicy: + return HighLevelPolicy(model_path, device=device, problem=problem) + + +__all__ = ["HighLevelPolicy", "load_high_policy"] diff --git a/project/policies/low_level_ppo.py b/project/policies/low_level_ppo.py new file mode 100644 index 0000000..974efb2 --- /dev/null +++ b/project/policies/low_level_ppo.py @@ -0,0 +1,9 @@ +"""兼容入口：低层八方向到达策略（离线导航实验）。""" +from mcm26b.hrl.policy import LowLevelPolicy + + +def load_low_policy(model_path: str, *, device: str = "auto") -> LowLevelPolicy: + return LowLevelPolicy(model_path, device=device) + + +__all__ = ["LowLevelPolicy", "load_low_policy"] diff --git a/project/train/__init__.py b/project/train/__init__.py new file mode 100644 index 0000000..cf0ca9e --- /dev/null +++ b/project/train/__init__.py @@ -0,0 +1,5 @@ +from .curriculum import STAGES, Stage, make_scene, training_config +from .domain_randomization import randomize_problem_config + +__all__ = ["STAGES", "Stage", "make_scene", "training_config", + "randomize_problem_config"] diff --git a/project/train/curriculum.py b/project/train/curriculum.py new file mode 100644 index 0000000..b99220d --- /dev/null +++ b/project/train/curriculum.py @@ -0,0 +1,6 @@ +"""兼容入口：课程学习与域随机化。""" +from mcm26b.hrl.curriculum import (STAGES, Stage, make_scene, stage_with_noise, + training_config, training_spec) + +__all__ = ["STAGES", "Stage", "make_scene", "stage_with_noise", + "training_config", "training_spec"] diff --git a/project/train/domain_randomization.py b/project/train/domain_randomization.py new file mode 100644 index 0000000..8577682 --- /dev/null +++ b/project/train/domain_randomization.py @@ -0,0 +1,31 @@ +"""兼容入口：本地域随机化参数包装。 + +当前真实随机化在 mcm26b.hrl.curriculum.make_scene 内完成；这里只暴露 +ProblemConfig 的显式替换助手，便于 Optuna/消融在实验配置中记录随机化范围。 +""" +from __future__ import annotations + +import dataclasses +from random import Random + +from mcm26b.config import ProblemConfig + + +def randomize_problem_config(base: ProblemConfig, *, seed: int, + bearing_error_half_deg: float | None = None, + r_eff_range: tuple[float, float] | None = None + ) -> ProblemConfig: + """返回带域随机化标量的新配置；不修改全局默认配置。""" + rng = Random(seed) + field = base.field + if bearing_error_half_deg is None: + bearing_error_half_deg = field.bearing_error_half_deg + if r_eff_range is not None: + lo, hi = r_eff_range + if not (0 <= lo <= hi): + raise ValueError("r_eff_range 必须为递增非负区间") + return dataclasses.replace(base, field=dataclasses.replace( + field, bearing_error_half_deg=float(bearing_error_half_deg))) + + +__all__ = ["randomize_problem_config"] diff --git a/project/train/optuna_tune.py b/project/train/optuna_tune.py new file mode 100644 index 0000000..b666668 --- /dev/null +++ b/project/train/optuna_tune.py @@ -0,0 +1,20 @@ +"""兼容入口：调用 scripts/tune_hrl.py（P2 后启用）。""" +from __future__ import annotations + +import importlib.util +from pathlib import Path + + +def run_tuning(argv=None) -> int: + path = Path(__file__).resolve().parents[2] / "scripts" / "tune_hrl.py" + if not path.exists(): + raise NotImplementedError( + "scripts/tune_hrl.py 尚未实现；P1 阶段先冻结超参并留待 P2。") + spec = importlib.util.spec_from_file_location("tune_hrl_compat", path) + module = importlib.util.module_from_spec(spec) + assert spec.loader is not None + spec.loader.exec_module(module) + return int(module.main(argv)) + + +__all__ = ["run_tuning"] diff --git a/pyproject.toml b/pyproject.toml index 636982d..5a31b2c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,11 @@ explicit = true [tool.uv.sources] mcm26b-referee = { workspace = true } -torch = { index = "pytorch-cu128" } +# CUDA 12.8 版 PyTorch 仅限 Windows/Linux（RTX 4060 训练机）； +# macOS (Apple Silicon, MPS) 回落到 PyPI 官方轮子。 +torch = [ + { index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, +] [build-system] requires = ["hatchling"] diff --git a/scripts/ablate_hrl.py b/scripts/ablate_hrl.py new file mode 100644 index 0000000..c038a62 --- /dev/null +++ b/scripts/ablate_hrl.py @@ -0,0 +1,25 @@ +"""HRL 消融入口（P2 待实现）。 + +预定义消融维度：时间目标 vs prompt 塑形、信息增益奖励系数、动作掩码取消范围、 +课程学习、粒子统计特征。硬性协议守卫/证书/预算永远保留，不在消融中取消。 +""" +from __future__ import annotations + +import argparse + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--variants", default="time,prompt,no_info,no_mask,no_curriculum") + parser.add_argument("--manifest", default="output/hrl/manifests/final.json") + parser.add_argument("--output", default="output/hrl/p2-ablation") + parser.add_argument("--timesteps", type=int, default=2048) + parser.add_argument("--seeds", type=int, default=5) + parser.parse_args(argv) + raise NotImplementedError( + "P2 尚未实现：必须先完成专家 BC 与冻结场景对照，" + "再对相同训练步数/场景/种子逐项消融；P1 只保证 benchmark/training/CLI。") + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/benchmark_hrl.py b/scripts/benchmark_hrl.py new file mode 100644 index 0000000..aa74bf6 --- /dev/null +++ b/scripts/benchmark_hrl.py @@ -0,0 +1,429 @@ +"""HRL/基线统一评测入口（P1）。 + +特点： +- 同一 manifest 中的真值场景被所有 variant 复用，保证配对比较； +- 真值仅在测试端用于构造 ``LocalSimulator`` 与最终核对，策略端只收到协议响应； +- 支持 ``hrl_heuristic``（高层启发式降级）、``bayes``、``directional``； +- 支持已训练高层模型 ``hrl``（需 ``--hrl-model`` 或 manifest 中的 variant 配置）。 + +输出目录（默认拒绝覆盖已有结果）： + /manifest.json + /runs///summary.json + /runs///session.jsonl + /results.json + /aggregate.json + /aggregate.csv + +示例： + python scripts/benchmark_hrl.py \ + --variants hrl_heuristic,bayes,directional \ + --manifest output/hrl/manifests/dev.json \ + --output output/hrl/p1-heuristic +""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import statistics +import sys +import time +from collections import defaultdict +from dataclasses import asdict, dataclass +from pathlib import Path +from typing import Any, Callable + +from mcm26b.config import ProblemConfig +from mcm26b.harness import LocalSimTransport, mixed_scene, random_omni_scene +from mcm26b.harness.scenes import referee_spec +from mcm26b.protocol import SimulatorClient +from mcm26b.protocol.log import SessionLog + +try: # 测试端依赖；运行时代码不导入 + from referee import LocalSimulator, Source +except ImportError as exc: # pragma: no cover + raise SystemExit("benchmark_hrl 需要 dev 依赖 referee。请先 `uv sync`。") from exc + + +# --------------------------------------------------------------------------- +# manifest / scene +# --------------------------------------------------------------------------- +@dataclass(frozen=True) +class SceneSpec: + scene_id: str + seed: int + error_seed: int + problem: int + sources: list[dict[str, Any]] + + @property + def truth_channels(self) -> tuple[int, ...]: + return tuple(sorted(int(src["channel"]) for src in self.sources)) + + +def _source_dict(source: Source) -> dict[str, Any]: + return { + "channel": int(source.channel), + "x": float(source.x), + "y": float(source.y), + "r_eff_m": float(source.r_eff_m), + "directional": bool(source.directional), + "heading_deg": float(source.heading_deg), + } + + +def _sources_to_dicts(sources: list[Source]) -> list[dict[str, Any]]: + return [_source_dict(source) for source in sources] + + +def _source_from_dict(data: dict[str, Any]) -> Source: + return Source( + channel=int(data["channel"]), + x=float(data["x"]), + y=float(data["y"]), + r_eff_m=float(data["r_eff_m"]), + directional=bool(data.get("directional", False)), + heading_deg=float(data.get("heading_deg", 0.0)), + ) + + +def make_default_manifest(*, problem: int, scenes: int, sources: int | None, + directional: int, start_seed: int, + config: ProblemConfig) -> dict[str, Any]: + """按 CLI 参数生成可复现的固定场景清单。""" + entries: list[dict[str, Any]] = [] + for index in range(scenes): + seed = start_seed + index + if problem == 4: + scene_sources = mixed_scene(seed=seed, count=sources, + directional=directional, config=config) + else: + scene_sources = random_omni_scene(seed=seed, count=sources, + config=config) + entries.append({ + "scene_id": f"scene-{seed}", + "seed": seed, + "error_seed": seed + 10_000, + "problem": problem, + "sources": _sources_to_dicts(scene_sources), + }) + return {"version": 1, "problem": problem, "scenes": entries} + + +def load_or_generate_manifest(path: Path, *, problem: int, scenes: int, + sources: int | None, directional: int, + start_seed: int, + config: ProblemConfig) -> dict[str, Any]: + if path.exists(): + manifest = json.loads(path.read_text(encoding="utf-8")) + else: + manifest = make_default_manifest( + problem=problem, scenes=scenes, sources=sources, + directional=directional, start_seed=start_seed, config=config) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), + encoding="utf-8") + if "scenes" not in manifest or not manifest["scenes"]: + raise ValueError(f"manifest 无 scenes: {path}") + for raw in manifest["scenes"]: + if not {"scene_id", "error_seed", "problem", "sources"} <= set(raw): + raise ValueError(f"manifest scene 字段缺失: {raw!r}") + if not raw["sources"]: + raise ValueError(f"scene {raw.get('scene_id')} 没有来源真值") + return manifest + + +def scenes_from_manifest(manifest: dict[str, Any]) -> list[SceneSpec]: + result: list[SceneSpec] = [] + for raw in manifest["scenes"]: + result.append(SceneSpec( + scene_id=str(raw["scene_id"]), + seed=int(raw.get("seed", 0)), + error_seed=int(raw["error_seed"]), + problem=int(raw["problem"]), + sources=[dict(item) for item in raw["sources"]], + )) + return result + + +# --------------------------------------------------------------------------- +# variants +# --------------------------------------------------------------------------- +def _build_loop(variant: str, client: SimulatorClient, config: ProblemConfig, + problem: int, *, hrl_model: str | None, + hrl_device: str = "auto"): + """延迟导入变体实现，避免 benchmark 顶层拉起重依赖。""" + if variant == "bayes": + from mcm26b.planner.bayes_loop import BayesLoop + return BayesLoop(client, config) + if variant == "directional": + from mcm26b.planner import DirectionalExtension + return DirectionalExtension(client, config) + if variant == "hrl_heuristic": + from mcm26b.planner.hrl_loop import HRLLoop + return HRLLoop(client, config, problem=problem, high_policy=None) + if variant == "hrl": + if not hrl_model: + raise RuntimeError("variant=hrl 需要 --hrl-model") + from mcm26b.hrl.policy import HighLevelPolicy + from mcm26b.planner.hrl_loop import HRLLoop + policy = HighLevelPolicy(hrl_model, device=hrl_device, problem=problem) + return HRLLoop(client, config, problem=problem, high_policy=policy) + raise ValueError(f"未知 variant: {variant!r}") + + +def _validity(summary: dict[str, Any], truth_channels: tuple[int, ...]) -> dict[str, Any]: + """测试端有效性审计。真值只在此处进入。""" + cleared = set(int(c) for c in summary.get("cleared_channels", [])) + absent = set(int(c) for c in summary.get("absent_channels", [])) + truth = set(truth_channels) + declared_absent = truth & absent + cleared_matches_truth = cleared == truth + no_source_declared_absent = not declared_absent + completed = bool(summary.get("completed")) + anomalies = list(summary.get("anomalies", [])) + # 本地 benchmark 要求：清洗真值且无守恒性异常；缺席集合不得含真值。 + valid = completed and cleared_matches_truth and no_source_declared_absent \ + and not anomalies + return { + "valid": valid, + "completed": completed, + "cleared_matches_truth": cleared_matches_truth, + "no_source_declared_absent": no_source_declared_absent, + "truth_channels": sorted(truth), + "declared_absent_truth": sorted(declared_absent), + "anomaly_count": len(anomalies), + } + + +def _row_from_summary(*, scene: SceneSpec, variant: str, summary: dict[str, Any], + wall_s: float) -> dict[str, Any]: + validity = _validity(summary, scene.truth_channels) + cleared_count = len(summary.get("cleared_channels", [])) + virtual_s = float(summary.get("virtual_time_s") or 0.0) + avg_s = summary.get("avg_locate_clear_time_s") + totals = summary.get("step_totals", {}) or {} + return { + "scene_id": scene.scene_id, + "variant": variant, + "problem": scene.problem, + "truth_count": len(scene.truth_channels), + "cleared_count": cleared_count, + "virtual_time_s": virtual_s, + "avg_locate_clear_time_s": (float(avg_s) if avg_s is not None else None), + "accounted_virtual_time_s": summary.get("accounted_virtual_time_s"), + "program_run_time_s": round(wall_s, 6), + "real_elapsed_total_s": totals.get("real_elapsed_total_s"), + "total_requests": summary.get("total_requests"), + "measures": summary.get("measures"), + "clears": summary.get("clears"), + "clear_success": summary.get("clear_success"), + "completed": bool(summary.get("completed")), + "stop_reason": summary.get("stop_reason"), + "exit_reason": summary.get("exit_reason"), + **validity, + } + + +def _error_row(scene: SceneSpec, variant: str, exc: BaseException) -> dict[str, Any]: + return { + "scene_id": scene.scene_id, + "variant": variant, + "problem": scene.problem, + "truth_count": len(scene.truth_channels), + "cleared_count": 0, + "virtual_time_s": None, + "avg_locate_clear_time_s": None, + "accounted_virtual_time_s": None, + "program_run_time_s": None, + "real_elapsed_total_s": None, + "total_requests": None, + "measures": None, + "clears": None, + "clear_success": None, + "completed": False, + "stop_reason": None, + "exit_reason": None, + "valid": False, + "cleared_matches_truth": False, + "no_source_declared_absent": False, + "truth_channels": sorted(scene.truth_channels), + "declared_absent_truth": [], + "anomaly_count": 0, + "error": f"{type(exc).__name__}: {exc}", + } + + +def run_scene_variant(scene: SceneSpec, variant: str, *, config: ProblemConfig, + hrl_model: str | None, run_dir: Path, + hrl_device: str = "auto") -> dict[str, Any]: + """运行一个 scene × variant。返回可序列化结果行与 summary。""" + run_dir.mkdir(parents=True, exist_ok=True) + sources = [_source_from_dict(raw) for raw in scene.sources] + simulator = LocalSimulator( + sources, + robot_id="hrl-benchmark", + spec=referee_spec(config), + error_seed=scene.error_seed, + stress_mode=False, + ) + # 真值只用于 LocalSimulator；策略端仅持有 client/loop，不读取 simulator.sources。 + log_path = run_dir / "session.jsonl" + log = SessionLog(path=log_path) + client = SimulatorClient("hrl-benchmark", config=config, + transport=LocalSimTransport(simulator), log=log) + loop = _build_loop(variant, client, config, scene.problem, + hrl_model=hrl_model, hrl_device=hrl_device) + started = time.monotonic() + try: + summary = loop.run() + finally: + wall_s = time.monotonic() - started + row = _row_from_summary(scene=scene, variant=variant, summary=summary, + wall_s=wall_s) + # 完整 summary 可能很大；仍写入 runs 目录，供复算与异常审计。 + (run_dir / "summary.json").write_text( + json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + return {"row": row, "summary": summary} + + +# --------------------------------------------------------------------------- +# aggregate +# --------------------------------------------------------------------------- +def _aggregate(rows: list[dict[str, Any]]) -> dict[str, Any]: + grouped: dict[str, list[dict[str, Any]]] = defaultdict(list) + for row in rows: + grouped[row["variant"]].append(row) + result: dict[str, Any] = {"variants": {}, "all_valid": True} + for variant, items in sorted(grouped.items()): + virtual = [float(item["virtual_time_s"]) for item in items + if item.get("virtual_time_s") is not None] + per_source = [float(item["avg_locate_clear_time_s"]) + for item in items + if item.get("avg_locate_clear_time_s") is not None] + cleared = [int(item["cleared_count"] or 0) for item in items] + program = [float(item["program_run_time_s"]) for item in items + if item.get("program_run_time_s") is not None] + success = [1.0 if item.get("valid") else 0.0 for item in items] + result["variants"][variant] = { + "episodes": len(items), + "valid_episodes": int(sum(success)), + "valid_rate": (sum(success) / len(items)) if items else 0.0, + "success_rate": (sum(success) / len(items)) if items else 0.0, + "cleared_count_mean": statistics.fmean(cleared) if cleared else 0.0, + "cleared_count_std": (statistics.pstdev(cleared) + if len(cleared) > 1 else 0.0), + "virtual_time_mean_s": statistics.fmean(virtual) if virtual else 0.0, + "virtual_time_std_s": (statistics.pstdev(virtual) + if len(virtual) > 1 else 0.0), + "virtual_time_min_s": min(virtual) if virtual else 0.0, + "virtual_time_max_s": max(virtual) if virtual else 0.0, + "avg_locate_clear_time_mean_s": (statistics.fmean(per_source) + if per_source else None), + "program_run_time_mean_s": (statistics.fmean(program) + if program else None), + "total_requests_mean": statistics.fmean( + [int(item.get("total_requests") or 0) for item in items]), + } + if result["variants"][variant]["valid_rate"] < 1.0: + result["all_valid"] = False + return result + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--variants", default="hrl_heuristic,bayes,directional", + help="逗号分隔：hrl_heuristic,bayes,directional,hrl") + parser.add_argument("--manifest", default="output/hrl/manifests/dev.json") + parser.add_argument("--output", default="output/hrl/p1-benchmark") + parser.add_argument("--problem", type=int, choices=(3, 4), default=3) + parser.add_argument("--scenes", type=int, default=6, + help="manifest 不存在时生成的场景数") + parser.add_argument("--sources", type=int, default=None, + help="每局源数；默认按题面 10~16 随机") + parser.add_argument("--directional", type=int, default=3, + help="问题4定向源数量（生成 manifest 时使用）") + parser.add_argument("--start-seed", type=int, default=91_000) + parser.add_argument("--hrl-model", default=None) + parser.add_argument("--hrl-device", choices=("auto", "cpu", "cuda", "mps"), + default="auto") + parser.add_argument("--force", action="store_true", + help="允许覆盖已有输出目录") + parser.add_argument("--log", action="store_true", + help="为兼容 CLI 保留；当前始终写 session.jsonl") + args = parser.parse_args(argv) + + config = ProblemConfig() + output = Path(args.output) + if output.exists() and any(output.iterdir()) and not args.force: + print(f"输出目录已存在且非空：{output}；如需覆盖请加 --force。", + file=sys.stderr) + return 2 + output.mkdir(parents=True, exist_ok=True) + manifest_path = Path(args.manifest) + manifest = load_or_generate_manifest( + manifest_path, problem=args.problem, scenes=args.scenes, + sources=args.sources, directional=args.directional, + start_seed=args.start_seed, config=config) + scenes = scenes_from_manifest(manifest) + variants = [item.strip() for item in args.variants.split(",") if item.strip()] + if not variants: + parser.error("--variants 不能为空") + if "hrl" in variants and not args.hrl_model: + # manifest 中也允许写明模型路径；此处先支持 CLI 显式路径。 + print("variant=hrl 需要 --hrl-model（P1 冒烟模型路径）。", file=sys.stderr) + return 2 + known = {"hrl_heuristic", "bayes", "directional", "hrl"} + unknown = sorted(set(variants) - known) + if unknown: + parser.error(f"未知 variant: {unknown}") + + rows: list[dict[str, Any]] = [] + runs_root = output / "runs" + wall_started = time.monotonic() + for scene in scenes: + for variant in variants: + run_dir = runs_root / scene.scene_id / variant + print(json.dumps({"event": "run_start", "scene": scene.scene_id, + "variant": variant}, ensure_ascii=False)) + try: + result = run_scene_variant( + scene, variant, config=config, hrl_model=args.hrl_model, + run_dir=run_dir, hrl_device=args.hrl_device) + row = result["row"] + except Exception as exc: # 单场异常也进入聚合，all_valid=false + row = _error_row(scene, variant, exc) + rows.append(row) + print(json.dumps({"event": "run_done", **{ + key: row.get(key) for key in + ("scene_id", "variant", "cleared_count", "virtual_time_s", + "valid", "program_run_time_s", "error")}}, ensure_ascii=False)) + aggregate = _aggregate(rows) + aggregate["wall_seconds"] = round(time.monotonic() - wall_started, 3) + aggregate["manifest"] = str(manifest_path) + aggregate["output"] = str(output) + aggregate["variants_requested"] = variants + aggregate["scenes"] = len(scenes) + (output / "results.json").write_text( + json.dumps(rows, ensure_ascii=False, indent=2), encoding="utf-8") + (output / "manifest.json").write_text( + json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8") + (output / "aggregate.json").write_text( + json.dumps(aggregate, ensure_ascii=False, indent=2), encoding="utf-8") + fieldnames = list(rows[0].keys()) + for row in rows: + for key in row: + if key not in fieldnames: + fieldnames.append(key) + with (output / "aggregate.csv").open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(rows) + print(json.dumps(aggregate, ensure_ascii=False, indent=2)) + return 0 if aggregate.get("all_valid") else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pretrain_hrl_expert.py b/scripts/pretrain_hrl_expert.py new file mode 100644 index 0000000..f784984 --- /dev/null +++ b/scripts/pretrain_hrl_expert.py @@ -0,0 +1,24 @@ +"""高层专家轨迹预训练入口（P2 待实现）。 + +按 docs/hrl-sol-execution.md，P2 才运行 `hrl_heuristic` 收集 +(episode_id, state, candidates, mask, action)，再按 episode 划分训练/验证集做 BC。 +本文件在 P0+P1 阶段只提供命令行占位，避免把未实现的 BC 说成已交付。 +""" +from __future__ import annotations + +import argparse + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--problem", type=int, choices=(3, 4), default=3) + parser.add_argument("--episodes", type=int, default=3000) + parser.add_argument("--output", default="output/hrl/bc_high") + parser.parse_args(argv) + raise NotImplementedError( + "P2 尚未实现：专家轨迹收集/BC 需要先通过 P1 smoke；" + "当前请使用 scripts/benchmark_hrl.py --variants hrl_heuristic 做启发式骨架验证。") + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/train_hrl_high.py b/scripts/train_hrl_high.py new file mode 100644 index 0000000..809e1d2 --- /dev/null +++ b/scripts/train_hrl_high.py @@ -0,0 +1,476 @@ +"""高层 MaskablePPO 训练（P1）。 + +架构与边界： +- 环境 = :class:`mcm26b.hrl.hierarchical_env.HierarchicalEnv`，每一步是一个高层 + 宏动作（选择 0–119 槽位：clear/locate/station/fallback/保留）； +- 训练/部署共用 :mod:`mcm26b.hrl.scheduler` 的候选、观测和动作掩码； +- 低层默认使用几何选点；``--local-selector rl`` 仅作为接口预留并记录， + 当前 HTTP 执行路径仍由高层宏动作终点决定，不把八方向推演当正式收益。 + +P1 smoke 示例： + python scripts/train_hrl_high.py \ + --problem 3 --stage 1 --timesteps 2048 --seed 320001 \ + --envs 1 --vec-env dummy --device mps \ + --config-profile production --model output/hrl/p1-smoke/q3.zip \ + --eval-episodes 2 --verbose 0 +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import platform +import random +import sys +import time +from pathlib import Path +from typing import Any + +from mcm26b.config import ProblemConfig +from mcm26b.hrl.curriculum import STAGES, training_config +from mcm26b.hrl.constants import (ACTION_LAYOUT_VERSION, FEATURE_VERSION, + HIGH_OBS_DIM, N_CANDIDATES, REWARD_VERSION) + + +def _stage_from_index(index: int): + if not 1 <= int(index) <= len(STAGES): + raise ValueError(f"stage 必须在 1..{len(STAGES)}") + return STAGES[int(index) - 1] + + +def _profile_fidelity(profile: str) -> str: + if profile == "production": + return "production" + if profile == "coarse-pretrain": + return "fast" + raise ValueError(f"未知 config-profile: {profile}") + + +def _reward_from_profile(profile: str): + from mcm26b.hrl.hierarchical_env import HRLReward + + if profile == "time": + return HRLReward.time_only() + if profile == "prompt": + return HRLReward() + raise ValueError(f"未知 reward-profile: {profile}") + + +def _make_env(problem: int, stage_index: int, seed: int, + monitor_path: str | None, profile: str, + reward_profile: str): + """顶层工厂：供 DummyVecEnv / SubprocVecEnv 调用（必须是可 pickle 的）。""" + from stable_baselines3.common.monitor import Monitor + + from mcm26b.hrl.hierarchical_env import HierarchicalEnv + + stage = _stage_from_index(stage_index) + env = HierarchicalEnv( + problem=problem, + stage=stage, + fidelity=_profile_fidelity(profile), + max_episode_steps=300, + real_budget_s=1800.0, + reward=_reward_from_profile(reward_profile), + ) + return Monitor(env, filename=monitor_path) + + +def _build_vec_env(args, monitor_paths: list[str]): + from stable_baselines3.common.vec_env import (DummyVecEnv, SubprocVecEnv) + + from functools import partial + + factories = [ + partial(_make_env, args.problem, args.stage, args.seed + rank, + monitor_paths[rank], args.config_profile, args.reward_profile) + for rank in range(args.envs) + ] + if args.vec_env == "subproc": + # 每个 worker 独立 Monitor 文件；spawn 入口在 __main__ 保护内。 + return SubprocVecEnv(factories, start_method="spawn") + return DummyVecEnv(factories) + + +def _hash_file(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def _model_metadata(*, args, wall_s: float, n_envs: int, + actual_timesteps: int) -> dict[str, Any]: + import numpy + import stable_baselines3 + import sb3_contrib + import torch + + stage = _stage_from_index(args.stage) + return { + "level": "high", + "problem": int(args.problem), + "stage": int(args.stage), + "stage_name": stage.name, + "algorithm": "maskable_ppo", + "feature_version": FEATURE_VERSION, + "action_layout_version": ACTION_LAYOUT_VERSION, + "reward_version": REWARD_VERSION, + "obs_dim": HIGH_OBS_DIM, + "act_dim": N_CANDIDATES, + "seed": int(args.seed), + "requested_timesteps": int(args.timesteps), + "timesteps_semantics": ("additional" if (args.resume or args.init) + else "total"), + "actual_timesteps": int(actual_timesteps), + "n_envs": int(n_envs), + "vec_env": args.vec_env, + "config_profile": args.config_profile, + "fidelity": _profile_fidelity(args.config_profile), + "reward_profile": args.reward_profile, + "reward_version": REWARD_VERSION, + "local_selector": args.local_selector, + "local_model": args.local_model, + "device": args.device, + "wall_seconds": round(wall_s, 3), + "max_wall_s": float(args.max_wall_s), + "created_unix_s": time.time(), + "python": sys.version.split()[0], + "platform": platform.platform(), + "torch": torch.__version__, + "numpy": numpy.__version__, + "stable_baselines3": stable_baselines3.__version__, + "sb3_contrib": sb3_contrib.__version__, + "mps_available": bool(getattr(getattr(torch.backends, "mps", None), + "is_available", lambda: False)()), + } + + +def _evaluate_env(model, *, problem: int, stage_index: int, + episodes: int, seed: int, profile: str, + reward_profile: str) -> dict[str, Any]: + from mcm26b.hrl.hierarchical_env import HierarchicalEnv + + stage = _stage_from_index(stage_index) + env = HierarchicalEnv(problem=problem, stage=stage, + fidelity=_profile_fidelity(profile), + max_episode_steps=300, real_budget_s=1800.0, + reward=_reward_from_profile(reward_profile)) + wins = 0 + rows = [] + if episodes <= 0: + env.close() + return {"episodes": 0, "successes": 0, "success_rate": None, "rows": []} + for episode in range(episodes): + obs, info = env.reset(seed=seed + episode) + done = False + steps = 0 + while not done: + action, _state = model.predict( + obs, action_masks=env.action_masks(), deterministic=True) + obs, reward, terminated, truncated, info = env.step(int(action)) + done = bool(terminated or truncated) + steps += 1 + # 课程阶段的小场景只需证明全部频道 accounted；完整阶段至少应清掉 + # 场景最少的题面源数。这里不读取真值，只使用环境自身统计。 + success = bool( + info.get("accounted", 0) >= 20 + and info.get("cleared", 0) >= stage.sources_min + and not info.get("anomalies") + ) + wins += int(success) + rows.append({ + "episode": episode, + "seed": seed + episode, + "steps": steps, + "cleared": info.get("cleared", 0), + "accounted": info.get("accounted", 0), + "virtual_time_s": info.get("episode_virtual_cost_s"), + "stop_reason": info.get("stop_reason"), + "anomalies": info.get("anomalies"), + "success": success, + }) + env.close() + return { + "episodes": len(rows), + "successes": wins, + "success_rate": wins / len(rows) if rows else 0.0, + "rows": rows, + } + + +def _evaluate_manifest(model_path: Path, manifest_path: str, *, + limit: int, problem: int) -> dict[str, Any]: + """可选：用 benchmark_hrl 的同一套真值隔离评测在固定 manifest 上跑已训模型。""" + scripts_dir = Path(__file__).resolve().parent + if str(scripts_dir) not in sys.path: + sys.path.insert(0, str(scripts_dir)) + import benchmark_hrl as bench # scripts/benchmark_hrl.py + + manifest = json.loads(Path(manifest_path).read_text(encoding="utf-8")) + scenes = bench.scenes_from_manifest(manifest) + if limit > 0: + scenes = scenes[:limit] + rows = [] + for scene in scenes: + run_dir = Path(model_path).parent / "eval-manifest" / scene.scene_id + result = bench.run_scene_variant( + scene, "hrl", config=ProblemConfig(), + hrl_model=str(model_path), run_dir=run_dir) + rows.append(result["row"]) + aggregate = bench._aggregate(rows) + return aggregate + + +def run_training(args) -> int: + import numpy as np + import torch + from sb3_contrib import MaskablePPO + from stable_baselines3.common.callbacks import BaseCallback + from stable_baselines3.common.utils import set_random_seed + + torch.set_num_threads(1) + set_random_seed(args.seed) + random.seed(args.seed) + np.random.seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(args.seed) + + model_path = Path(args.model) + if model_path.exists() and not (args.resume or args.force): + print(f"模型已存在：{model_path}；如需覆盖请加 --force，续训请加 --resume。", + file=sys.stderr) + return 2 + model_path.parent.mkdir(parents=True, exist_ok=True) + run_dir = model_path.parent + monitor_paths = [ + str(run_dir / f"high_curve_{model_path.stem}_env{rank}.monitor.csv") + for rank in range(args.envs) + ] + for path in monitor_paths: + Path(path).parent.mkdir(parents=True, exist_ok=True) + + vec_env = _build_vec_env(args, monitor_paths) + + if args.resume: + if not model_path.exists(): + print(f"--resume 指定的模型不存在：{model_path}", file=sys.stderr) + return 2 + model = MaskablePPO.load(str(model_path), env=vec_env, + device=args.device, + print_system_info=False) + elif args.init: + init_path = Path(args.init) + if not init_path.exists(): + print(f"--init 指定的模型不存在：{init_path}", file=sys.stderr) + return 2 + model = MaskablePPO.load(str(init_path), env=vec_env, + device=args.device, + print_system_info=False) + else: + model = MaskablePPO( + "MlpPolicy", + vec_env, + policy_kwargs={"net_arch": [128, 128]}, + n_steps=args.rollout_steps, + batch_size=args.batch_size, + n_epochs=args.n_epochs, + gamma=1.0, + gae_lambda=0.95, + learning_rate=3e-4, + clip_range=0.2, + ent_coef=0.005, + vf_coef=0.5, + max_grad_norm=0.5, + seed=args.seed, + device=args.device, + verbose=args.verbose, + ) + if args.init: + pass # 已在上面处理；此分支仅保持语义清晰 + + class _WallClockStop(BaseCallback): + def __init__(self, max_wall_s: float): + super().__init__() + self.max_wall_s = max_wall_s + self.start = time.monotonic() + + def _on_step(self) -> bool: + if time.monotonic() - self.start >= self.max_wall_s: + print(json.dumps({"event": "wall_clock_stop", + "elapsed_s": time.monotonic() - self.start})) + return False + return True + + class _ProgressJSON(BaseCallback): + """向训练前端输出单行 JSON 进度；不影响 SB3 自身 logger。 + + 前端首个 curve 点越早出现越好，因此开头几步按更小间隔发送（前 8 个 + 进度点逐步放大到 ``every_steps``），之后稳定在 ``every_steps``。 + 这样短程 smoke 也能看到完整曲线，长训不会刷屏。 + """ + + def __init__(self, every_steps: int): + super().__init__() + self.every_steps = max(1, int(every_steps)) + self.last_emit = int(model.num_timesteps) + self.emitted = 0 + + def _next_gap(self) -> int: + warmup = max(0.2, 1.0 - self.emitted / 8.0) + return max(1, min(self.every_steps, int(round(self.every_steps * warmup)))) + + def _on_step(self) -> bool: + current = int(self.model.num_timesteps) + if current - self.last_emit < self._next_gap(): + return True + self.last_emit = current + self.emitted += 1 + values = getattr(self.model.logger, "name_to_value", {}) or {} + + def _num(key, default=None): + value = values.get(key) + if value is None: + return default + try: + value = float(value) + except (TypeError, ValueError): + return default + if value != value or value in (float("inf"), float("-inf")): + return default + return value + + ep_rewards = [] + ep_lengths = [] + for item in getattr(self.model, "ep_info_buffer", []) or []: + try: + ep_rewards.append(float(item.get("r"))) + ep_lengths.append(float(item.get("l"))) + except (TypeError, ValueError, AttributeError): + continue + payload = { + "event": "progress", + "timesteps": current, + "n_updates": int(getattr(self.model, "_n_updates", 0) or 0), + "ep_rew_mean": (sum(ep_rewards) / len(ep_rewards) + if ep_rewards else None), + "ep_len_mean": (sum(ep_lengths) / len(ep_lengths) + if ep_lengths else None), + "value_loss": _num("train/value_loss"), + "policy_gradient_loss": _num("train/policy_gradient_loss"), + "approx_kl": _num("train/approx_kl"), + "entropy_loss": _num("train/entropy_loss"), + "explained_variance": _num("train/explained_variance"), + "fps": _num("time/fps"), + } + print(json.dumps(payload, ensure_ascii=False, allow_nan=False), + flush=True) + return True + + progress_every = (args.progress_every if args.progress_every > 0 + else args.rollout_steps * args.envs) + callbacks = [_WallClockStop(args.max_wall_s)] + if args.progress_json: + callbacks.insert(0, _ProgressJSON(progress_every)) + + started = time.monotonic() + actual_timesteps = int(model.num_timesteps) + try: + model.learn(total_timesteps=args.timesteps, + callback=callbacks if len(callbacks) > 1 else callbacks[0], + progress_bar=False, + reset_num_timesteps=not (args.resume or args.init)) + except Exception as exc: # 训练失败也必须保存失败上下文，不静默 + failure = { + "event": "training_failed", + "error": f"{type(exc).__name__}: {exc}", + "model": str(model_path), + } + (run_dir / "training_failure.json").write_text( + json.dumps(failure, ensure_ascii=False, indent=2), encoding="utf-8") + raise + wall_s = time.monotonic() - started + actual_timesteps = int(model.num_timesteps) + model.save(str(model_path)) + metadata = _model_metadata(args=args, wall_s=wall_s, + n_envs=args.envs, + actual_timesteps=actual_timesteps) + metadata["model_sha256"] = _hash_file(model_path) + metadata["monitor_files"] = monitor_paths + metadata["reward_note"] = ( + "reward_profile=time 使用 HRLReward.time_only（P0.4 纯时间口径）；" + "reward_profile=prompt 使用原始 §5.2 塑形。模型元数据已固化口径。") + metadata_path = model_path.with_suffix(".json") + metadata_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2), + encoding="utf-8") + vec_env.close() + + evaluation = _evaluate_env( + model, problem=args.problem, stage_index=args.stage, + episodes=args.eval_episodes, seed=args.seed + 1_000_000, + profile=args.config_profile, reward_profile=args.reward_profile) + manifest_eval = None + if args.eval_manifest: + try: + manifest_eval = _evaluate_manifest( + model_path, args.eval_manifest, limit=args.eval_manifest_limit, + problem=args.problem) + except Exception as exc: + manifest_eval = {"error": f"{type(exc).__name__}: {exc}"} + metadata["evaluation"] = evaluation + metadata["eval_manifest"] = manifest_eval + metadata_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2), + encoding="utf-8") + print(json.dumps(metadata, ensure_ascii=False, indent=2)) + return 0 + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--problem", type=int, choices=(3, 4), required=True) + parser.add_argument("--stage", type=int, choices=(1, 2, 3, 4), + default=4) + parser.add_argument("--timesteps", type=int, required=True) + parser.add_argument("--seed", type=int, default=320001) + parser.add_argument("--envs", type=int, default=1) + parser.add_argument("--vec-env", choices=("dummy", "subproc"), default="dummy") + parser.add_argument("--device", choices=("cpu", "cuda", "mps", "auto"), + default="auto") + parser.add_argument("--config-profile", + choices=("production", "coarse-pretrain"), + default="production") + parser.add_argument("--model", required=True) + parser.add_argument("--resume", action="store_true") + parser.add_argument("--init", default=None, + help="可选：Q3→Q4 或继续预训练权重路径") + parser.add_argument("--force", action="store_true") + parser.add_argument("--eval-manifest", default=None) + parser.add_argument("--eval-manifest-limit", type=int, default=0) + parser.add_argument("--eval-episodes", type=int, default=5) + parser.add_argument("--local-selector", choices=("geometry", "rl"), + default="geometry") + parser.add_argument("--local-model", default=None) + parser.add_argument("--rollout-steps", type=int, default=256) + parser.add_argument("--batch-size", type=int, default=256) + parser.add_argument("--n-epochs", type=int, default=10) + parser.add_argument("--max-wall-s", type=float, default=1200.0, + help="训练墙钟硬上限；默认 20 分钟") + parser.add_argument("--reward-profile", choices=("time", "prompt"), + default="time", + help="time=P0.4 纯时间口径；prompt=原始 §5.2 塑形对照") + parser.add_argument("--progress-json", action="store_true", + help="向训练前端输出单行 JSON 进度（UI 自动启用）") + parser.add_argument("--progress-every", type=int, default=0, + help="JSON 进度间隔环境步数；0=每个 rollout") + parser.add_argument("--verbose", type=int, default=1) + args = parser.parse_args(argv) + if args.timesteps <= 0 or args.envs <= 0: + parser.error("timesteps 与 envs 必须为正") + if args.envs * args.rollout_steps < 2: + parser.error("PPO rollout 至少需要 2 个样本") + if args.eval_episodes < 0: + parser.error("eval-episodes 不能为负") + return run_training(args) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/train_hrl_low.py b/scripts/train_hrl_low.py new file mode 100644 index 0000000..ea2a7ca --- /dev/null +++ b/scripts/train_hrl_low.py @@ -0,0 +1,267 @@ +"""低层 PPO 训练：8 方向到达任务（BC 直线专家热启动 + PPO 微调）。 + +产物： + output/hrl/hrl_low.zip / hrl_low.json PPO 模型（部署用） + output/hrl/bc_low_expert.npz / bc_low.pt 行为克隆数据与网络 + output/hrl/hrl_low_curve_env*.monitor.csv 每个环境独立学习曲线 + +评估口径：到达成功率 + 路径效率（直线距离/实际路径长，1 为完美直线）。 +这是独立导航实验：目前低层 PPO 仅供轨迹诊断，不影响正式 HTTP 任务。 + +示例： + uv run python scripts/train_hrl_low.py --timesteps 60000 + uv run python scripts/train_hrl_low.py --skip-bc # 纯 PPO 对照 +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import time +from pathlib import Path + +from mcm26b.config import ProblemConfig +from mcm26b.hrl.constants import (FEATURE_VERSION, LOW_MAX_STEPS, + LOW_NUM_ACTIONS, LOW_OBS_DIM) +from mcm26b.hrl.low_level_env import LowLevelEnv + +HIDDEN = (64, 64) + + +def _bc_module(obs_dim: int, n_actions: int, hidden: tuple[int, ...]): + import torch + + class BCMlp(torch.nn.Module): + def __init__(self): + super().__init__() + layers = [] + prev = obs_dim + for width in hidden: + layers += [torch.nn.Linear(prev, width), torch.nn.Tanh()] + prev = width + layers.append(torch.nn.Linear(prev, n_actions)) + self.net = torch.nn.Sequential(*layers) + + def forward(self, x): + return self.net(x) + + return BCMlp + + +def collect(episodes: int, seed: int, config: ProblemConfig): + import numpy as np + env = LowLevelEnv(config=config, seed=seed) + observations, actions, episode_ids = [], [], [] + for episode in range(episodes): + obs, _info = env.reset(seed=seed + episode) + for _step in range(LOW_MAX_STEPS + 2): + observations.append(obs) + actions.append(env.expert_action()) + episode_ids.append(episode) + obs, _r, terminated, truncated, _info = env.step(env.expert_action()) + if terminated or truncated: + break + env.close() + return (np.asarray(observations, dtype=np.float32), + np.asarray(actions, dtype=np.int64), + np.asarray(episode_ids, dtype=np.int64)) + + +def bc_validation_mask(episode_ids, *, seed: int): + """按整局留出，避免同一轨迹相邻状态跨训练/验证集。""" + import numpy as np + unique = np.unique(episode_ids) + if len(unique) < 2: + raise ValueError("BC 至少需要 2 局才能按整局划分训练/验证集") + rng = np.random.default_rng(seed) + validation_ids = rng.permutation(unique)[:max(1, len(unique) // 10)] + return np.isin(episode_ids, validation_ids) + + +def train_bc(x, y, episode_ids, *, epochs: int, batch_size: int, lr: float, + device: str, seed: int): + import numpy as np + import torch + BCMlp = _bc_module(LOW_OBS_DIM, LOW_NUM_ACTIONS, HIDDEN) + model = BCMlp().to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=lr) + valid = torch.as_tensor( + bc_validation_mask(episode_ids, seed=seed), device=device) + x_t = torch.as_tensor(x, device=device) + y_t = torch.as_tensor(y, device=device) + x_v, y_v, x_tr, y_tr = x_t[valid], y_t[valid], x_t[~valid], y_t[~valid] + loss_fn = torch.nn.CrossEntropyLoss() + for epoch in range(epochs): + order = torch.randperm(len(x_tr), device=device) + total = correct = 0.0 + for start in range(0, len(order), batch_size): + idx = order[start:start + batch_size] + logits = model(x_tr[idx]) + loss = loss_fn(logits, y_tr[idx]) + optimizer.zero_grad() + loss.backward() + optimizer.step() + total += float(loss.item()) * len(idx) + correct += int((logits.argmax(1) == y_tr[idx]).sum().item()) + if epoch == 0 or (epoch + 1) % 5 == 0: + with torch.no_grad(): + valid_acc = float((model(x_v).argmax(1) == y_v).float().mean().item()) + print(json.dumps({"event": "bc_epoch", "epoch": epoch + 1, + "loss": round(total / len(x_tr), 4), + "train_acc": round(correct / len(x_tr), 4), + "valid_acc": round(valid_acc, 4)}, + ensure_ascii=False)) + with torch.no_grad(): + valid_acc = float((model(x_v).argmax(1) == y_v).float().mean().item()) + return model, valid_acc + + +def copy_into_ppo(bc_model, device: str, env, *, seed: int, + n_steps: int = 512) -> object: + """把 BC 权重拷入 MaskablePPO（policy_net 两层 + action_net），前向校验。""" + import torch + from sb3_contrib import MaskablePPO + ppo = MaskablePPO("MlpPolicy", env, policy_kwargs={"net_arch": list(HIDDEN)}, + n_steps=n_steps, batch_size=128, n_epochs=6, + gamma=1.0, gae_lambda=0.95, learning_rate=3e-4, + clip_range=0.2, ent_coef=0.005, vf_coef=0.5, + max_grad_norm=0.5, seed=seed, device=device) + state = {key.removeprefix("net."): value + for key, value in bc_model.state_dict().items()} + policy_state = {key: value for key, value in state.items() + if key in ("0.weight", "0.bias", "2.weight", "2.bias")} + ppo.policy.mlp_extractor.policy_net.load_state_dict(policy_state) + ppo.policy.action_net.load_state_dict( + {"weight": state["4.weight"], "bias": state["4.bias"]}) + probe = torch.randn((8, LOW_OBS_DIM), device=device) + with torch.no_grad(): + copied = ppo.policy.action_net(ppo.policy.mlp_extractor.policy_net(probe)) + if not torch.allclose(bc_model(probe), copied, atol=1e-6): + raise RuntimeError("行为克隆与 PPO 前向映射不一致") + return ppo + + +def evaluate(model, *, episodes: int, seed: int, + config: ProblemConfig) -> dict: + """确定性策略评估：成功率与路径效率（对照直线）。""" + env = LowLevelEnv(config=config, seed=seed) + successes = 0 + efficiencies = [] + for episode in range(episodes): + obs, info = env.reset(seed=seed + episode) + start, goal = tuple(info["position"]), tuple(info["goal"]) + straight = math.dist(start, goal) + steps = 0 + for _ in range(LOW_MAX_STEPS + 2): + action, _state = model.predict(obs, deterministic=True) + obs, _r, terminated, truncated, info = env.step(int(action)) + steps += 1 + if terminated or truncated: + break + successes += int(terminated) + efficiencies.append(min(1.0, straight / + max(info["path_length_m"], 1e-9))) + env.close() + return {"episodes": episodes, + "success_rate": successes / episodes, + "mean_path_efficiency": sum(efficiencies) / len(efficiencies)} + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--timesteps", type=int, default=60_000) + parser.add_argument("--seed", type=int, default=20260911) + parser.add_argument("--bc-episodes", type=int, default=600) + parser.add_argument("--bc-epochs", type=int, default=12) + parser.add_argument("--envs", type=int, default=4) + parser.add_argument("--rollout-steps", type=int, default=512, + help="每环境每次 PPO rollout 长度；短 smoke 可设 8") + parser.add_argument("--device", default="cpu") + parser.add_argument("--model", default="output/hrl/hrl_low.zip") + parser.add_argument("--eval-episodes", type=int, default=40) + parser.add_argument("--skip-bc", action="store_true", + help="跳过行为克隆（纯 PPO 对照实验用）") + parser.add_argument("--verbose", type=int, default=1) + args = parser.parse_args(argv) + if min(args.envs, args.timesteps, args.eval_episodes, + args.rollout_steps) <= 0: + parser.error("envs/timesteps/eval-episodes/rollout-steps 必须为正数") + if args.envs * args.rollout_steps < 2: + parser.error("PPO rollout 至少需要 2 个样本") + if not args.skip_bc and (args.bc_episodes < 2 or args.bc_epochs < 1): + parser.error("BC 至少需要 2 局和 1 个 epoch") + + import torch + torch.set_num_threads(1) + from sb3_contrib import MaskablePPO + from stable_baselines3.common.monitor import Monitor + from stable_baselines3.common.vec_env import DummyVecEnv + from stable_baselines3.common.utils import set_random_seed + set_random_seed(args.seed) + + model_path = Path(args.model) + model_path.parent.mkdir(parents=True, exist_ok=True) + curve_paths = [model_path.with_name( + f"{model_path.stem}_curve_env{rank}.monitor.csv") + for rank in range(args.envs)] + + def _factory(rank: int): + def _make(): + env = Monitor(LowLevelEnv(seed=args.seed + rank), + filename=str(curve_paths[rank])) + return env + return _make + + vec_env = DummyVecEnv([_factory(rank) for rank in range(args.envs)]) + started = time.monotonic() + bc_valid_acc = None + if args.skip_bc: + model = MaskablePPO("MlpPolicy", vec_env, + policy_kwargs={"net_arch": list(HIDDEN)}, + n_steps=args.rollout_steps, batch_size=128, n_epochs=6, + gamma=1.0, gae_lambda=0.95, learning_rate=3e-4, + clip_range=0.2, ent_coef=0.005, vf_coef=0.5, + max_grad_norm=0.5, seed=args.seed, + device=args.device, verbose=args.verbose) + else: + x, y, episode_ids = collect(args.bc_episodes, args.seed, ProblemConfig()) + bc_model, bc_valid_acc = train_bc( + x, y, episode_ids, epochs=args.bc_epochs, batch_size=256, + lr=3e-4, device=args.device, seed=args.seed) + model = copy_into_ppo(bc_model, args.device, vec_env, seed=args.seed, + n_steps=args.rollout_steps) + bc_dir = model_path.parent / "bc_low.pt" + torch.save(bc_model.state_dict(), str(bc_dir)) + print(json.dumps({"event": "bc_done", "samples": int(len(x))}, + ensure_ascii=False)) + model.learn(total_timesteps=args.timesteps, + progress_bar=args.verbose > 0) + elapsed_s = time.monotonic() - started + + model.save(str(model_path)) + eval_seed = args.seed + 1_000_000 + evaluation = evaluate(model, episodes=args.eval_episodes, seed=eval_seed, + config=ProblemConfig()) + report = { + "level": "low", "timesteps": args.timesteps, "seed": args.seed, + "device": args.device, "wall_seconds": round(elapsed_s, 1), + "model": str(model_path), "algorithm": "maskable_ppo", + "feature_version": FEATURE_VERSION, "bc_skipped": args.skip_bc, + "bc_valid_acc": bc_valid_acc, "evaluation_seed": eval_seed, + "monitor_files": [str(path) for path in curve_paths], + "training_role": "standalone_navigation_only", + "obs_dim": LOW_OBS_DIM, "act_dim": LOW_NUM_ACTIONS, + "model_sha256": hashlib.sha256(model_path.read_bytes()).hexdigest(), + **evaluation, + } + model_path.with_suffix(".json").write_text( + json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8") + print(json.dumps(report, ensure_ascii=False, indent=2)) + vec_env.close() + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/train_hrl_ui.py b/scripts/train_hrl_ui.py new file mode 100644 index 0000000..bc0cbb5 --- /dev/null +++ b/scripts/train_hrl_ui.py @@ -0,0 +1,5 @@ +"""启动 HRL 模型训练前端（薄入口，便于加入支撑材料命令）。""" +from mcm26b.training_ui.server import run_training_ui + +if __name__ == "__main__": + raise SystemExit(run_training_ui()) diff --git a/scripts/tune_hrl.py b/scripts/tune_hrl.py new file mode 100644 index 0000000..b5c40eb --- /dev/null +++ b/scripts/tune_hrl.py @@ -0,0 +1,27 @@ +"""HRL Optuna TPE 调参入口（P2 待实现）。 + +搜索空间按用户 Prompt §10，但只允许在不破坏覆盖证明/证书/预算的工程参数上搜索： +粒子数、重采样阈值、信息增益权重、移动成本权重、检测成本权重、候选点距离、 +覆盖网格间距、停止连续无信号次数、清除距离阈值。目标 = 平均定位清除时间 + +未清除惩罚，每个配置至少 5 个随机种子。 +""" +from __future__ import annotations + +import argparse + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--problem", type=int, choices=(3, 4), default=3) + parser.add_argument("--trials", type=int, default=30) + parser.add_argument("--timesteps", type=int, default=2048) + parser.add_argument("--manifest", default="output/hrl/manifests/dev.json") + parser.add_argument("--output", default="output/hrl/p2-tuning") + parser.parse_args(argv) + raise NotImplementedError( + "P2 尚未实现：调参必须建立在 P1 smoke 与稳定基准之上，" + "且不能搜索真实误差上限或破坏覆盖证明的网格参数。") + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/mcm26b/cli.py b/src/mcm26b/cli.py index 353588a..f47038b 100644 --- a/src/mcm26b/cli.py +++ b/src/mcm26b/cli.py @@ -257,19 +257,38 @@ def _run(args) -> int: else: strategy = args.strategy loop_cls = Q3_STRATEGIES.get(strategy) + high_policy = None + low_policy = None try: if strategy == "rl": # 惰性导入：默认运行闭环不加载 numpy/torch/stable-baselines3。 from .planner.rl_loop import RLBayesLoop, RLDirectionalLoop loop_cls = RLBayesLoop if args.problem == 3 else RLDirectionalLoop + elif strategy == "hrl": + # P1：高层 MaskablePPO 调度。缺少模型时显式报错，不降级伪装。 + if not args.hrl_model: + raise RuntimeError( + "--strategy hrl/--q4-strategy hrl 需要 --hrl-model；" + "无模型启发式请使用 benchmark_hrl.py 的 hrl_heuristic 变体。") + from .hrl.policy import HighLevelPolicy, LowLevelPolicy + from .planner.hrl_loop import HRLLoop + high_policy = HighLevelPolicy( + args.hrl_model, device=args.hrl_device, problem=args.problem) + if args.hrl_low_model: + low_policy = LowLevelPolicy(args.hrl_low_model, device=args.hrl_device) + loop_cls = HRLLoop if loop_cls is None: raise RuntimeError(f"未知策略: {strategy}") if strategy == "rl": summary = loop_cls(client, config, model_path=args.rl_model).run() + elif strategy == "hrl": + summary = loop_cls( + client, config, problem=args.problem, high_policy=high_policy, + low_policy=low_policy, trace_executor=args.trace_low).run() else: summary = loop_cls(client, config).run() except RuntimeError as exc: - print(f"RL 策略不可用: {exc}", file=sys.stderr) + print(f"策略不可用: {exc}", file=sys.stderr) return 2 finally: if judge is not None: @@ -323,19 +342,30 @@ def main(argv=None) -> int: run.add_argument("--log", default=None, help="会话日志 JSONL 输出路径") run.add_argument("--problem", type=int, choices=(3, 4), default=3, help="问题3=全向源，问题4=混合定向源（默认 3）") - run.add_argument("--strategy", choices=tuple(Q3_STRATEGIES) + ("rl",), + run.add_argument("--strategy", choices=tuple(Q3_STRATEGIES) + ("rl", "hrl"), default="bayes", help="问题3策略：bayes=粒子滤波+信息增益+滚动调度（默认，" "自建消融相对 seven 再降 19.5%%），seven=七点批量，" - "grid=49点网格基线，rl=PPO 强化学习选点（需先训练模型）；" + "grid=49点网格基线，rl=PPO 强化学习选点（需先训练模型），" + "hrl=高层 MaskablePPO 分层调度（需 --hrl-model）；" "仅 --problem 3 生效") - run.add_argument("--q4-strategy", choices=tuple(Q4_STRATEGIES) + ("rl",), + run.add_argument("--q4-strategy", choices=tuple(Q4_STRATEGIES) + ("rl", "hrl"), default="directional", help="问题4策略：directional=49点覆盖+遮挡梯子+后备清除（默认），" - "rl=PPO 强化学习选点（需先训练模型）；仅 --problem 4 生效") + "rl=PPO 强化学习选点（需先训练模型），" + "hrl=高层 MaskablePPO 分层调度（需 --hrl-model）；仅 --problem 4 生效") run.add_argument("--rl-model", default=None, help="RL 模型路径（默认 output/rl/q3_ppo.zip 或 " "output/rl/q4_ppo.zip；训练脚本 scripts/train_rl.py）") + run.add_argument("--hrl-model", default=None, + help="高层 HRL MaskablePPO 模型路径（--strategy hrl 时必填；" + "训练脚本 scripts/train_hrl_high.py）") + run.add_argument("--hrl-low-model", default=None, + help="可选低层 HRL 模型；当前只用于轨迹推演留痕，不改变协议终点") + run.add_argument("--hrl-device", choices=("auto", "cpu", "cuda", "mps"), + default="auto", help="HRL 模型推理设备") + run.add_argument("--trace-low", action="store_true", + help="开启低层到达推演留痕（本地虚拟轨迹，不增加协议耗时）") run.add_argument("--bearing-margin-deg", type=float, default=0.005, help="示向度舍入裕量（服务器保留两位小数；默认 0.005°）") run.add_argument("--real-budget-s", type=float, default=None, diff --git a/src/mcm26b/hrl/__init__.py b/src/mcm26b/hrl/__init__.py new file mode 100644 index 0000000..4f33090 --- /dev/null +++ b/src/mcm26b/hrl/__init__.py @@ -0,0 +1,14 @@ +"""分层强化学习实验：整场子目标调度环境与独立的八方向到达实验。 + +与 :mod:`mcm26b.rl`（单步 PPO 选点）的差异：高层在整场 10–16 源的统一 +候选槽位上做调度决策（证书清除 / 信息增益定位 / 覆盖站扫频 / 后备清除）， +当前运行闭环将子目标端点提交协议，八方向策略仅用于可选本地路径留痕， +不影响真实请求及虚拟计时。完整高层训练、CLI 与配对评估仍待接通； +优化架构及逐项验收见 docs/hrl-sol-execution.md。 + +信念（belief + 网格幸存集）、证书审批与后备清除复用 planner 基础设施； +每次改动仍须验证真源保留和真实全清，不能用内部状态自洽替代评测。 + +本包属于训练/推理侧组件，运行时对依赖保持惰性（stable-baselines3 / +sb3-contrib 仅在显式加载模型时导入）。 +""" diff --git a/src/mcm26b/hrl/constants.py b/src/mcm26b/hrl/constants.py new file mode 100644 index 0000000..17704ad --- /dev/null +++ b/src/mcm26b/hrl/constants.py @@ -0,0 +1,49 @@ +"""HRL 观测/动作空间常量：模型接口的唯一字面量来源。 + +槽位布局（训练与推理共享，改动任一值都会使已训练模型失配，须同步重训）： + + 0–19 clear 频道 c 的证书清除（槽位 = c-1，证书就绪才有效） + 20–39 locate 频道 c 的定位测量（信息增益选点或定向遮挡梯子探点） + 40–88 station 覆盖站（问题3：七点环 0–6 有效；问题4：49 点网格） + 89–108 fallback 频道 c 的后备网格清除（仅问题4，遮挡梯子耗尽后开放） + 109–119 保留（恒屏蔽） + +高层观测 = 全局 8 维 + 20 频道 × 14 维 + 120 槽位 × 8 维 = 1248 维； +低层观测 10 维、动作 Discrete(8)（8 方向，步长 ≤100 m）。 +""" + +FEATURE_VERSION = 4 +ACTION_LAYOUT_VERSION = 1 +REWARD_VERSION = 1 +CHANNEL_COUNT = 20 + +# ------------------------------------------------------------ 高层动作槽位 +CLEAR_SLOTS = CHANNEL_COUNT +LOCATE_SLOTS = CHANNEL_COUNT +STATION_SLOTS = 49 +FALLBACK_SLOTS = CHANNEL_COUNT +CLEAR_OFFSET = 0 +LOCATE_OFFSET = CLEAR_OFFSET + CLEAR_SLOTS +STATION_OFFSET = LOCATE_OFFSET + LOCATE_SLOTS +FALLBACK_OFFSET = STATION_OFFSET + STATION_SLOTS +N_CANDIDATES = 120 + +# ------------------------------------------------------------ 高层观测 +PER_CHANNEL_DIM = 14 +HIGH_GLOBAL_DIM = 8 +PER_CANDIDATE_DIM = 8 +HIGH_OBS_DIM = (HIGH_GLOBAL_DIM + CHANNEL_COUNT * PER_CHANNEL_DIM + + N_CANDIDATES * PER_CANDIDATE_DIM) # 1248 + +# ------------------------------------------------------------ 低层接口 +LOW_OBS_DIM = 10 +LOW_NUM_ACTIONS = 8 # 8 方向，45° 等分 +LOW_STEP_M = 100.0 # 每步最大步长 [题面 低层动作 50–100 m] +LOW_REACH_M = 20.0 # 到达阈值（与清除半径一致） +LOW_MAX_STEPS = 60 # 直径穿越 ~51 步上界 + +# ------------------------------------------------------------ 观测归一化 +POS_SCALE_M = 2000.0 # 位置/位移归一化尺度（Ω 半径 1800 m 外扩） +RADIUS_SCALE_M = 2000.0 +COST_SCALE_S = 600.0 # 槽位预计服务耗时归一化尺度 +MEASURE_COUNT_SCALE = 12.0 diff --git a/src/mcm26b/hrl/curriculum.py b/src/mcm26b/hrl/curriculum.py new file mode 100644 index 0000000..6649793 --- /dev/null +++ b/src/mcm26b/hrl/curriculum.py @@ -0,0 +1,109 @@ +"""课程学习与域随机化：训练场景按阶段由易到难（prompt §7）。 + +四阶段课程：1 源/5 频道/无噪声 → 3 源/10 频道/±0.5° → 8 源/16 频道/±1° +→ 10–16 源/20 频道/±1°（问题4 最后阶段按比例混入定向源）。域随机化 +覆盖源位置（Ω 圆域均匀）、频道（互异抽样）、有效接收半径、朝向与逐局 +误差种子 [prompt §7.2]；机器狗初始位置固定 (0,0)——协议语义如此， +随机化它反而制造训练-部署差异（与 v1"训练部署特征错位"教训一致）。 + +真值只进入训练/评估场景生成，不进入任何观测；策略端仅见模拟器响应。 +""" + +from __future__ import annotations + +import dataclasses +import math +from dataclasses import dataclass +from random import Random + +from ..config import ProblemConfig + + +@dataclass(frozen=True) +class Stage: + """课程阶段参数：源数范围、频道上限、示向度噪声与定向源比例。""" + + name: str + sources_min: int + sources_max: int + channel_max: int # 频道 > channel_max 的频道保证无源 + bearing_error_half_deg: float + directional_ratio: float # 问题4 最后阶段定向源比例；问题3 恒 0 + recommended_timesteps: int + + +STAGES: tuple[Stage, ...] = ( + Stage("s1-single", 1, 1, 5, 0.0, 0.0, 60_000), + Stage("s2-few", 3, 3, 10, 0.5, 0.0, 80_000), + Stage("s3-many", 8, 8, 16, 1.0, 0.0, 100_000), + Stage("s4-full", 10, 16, 20, 1.0, 0.4, 140_000), +) + + +def stage_with_noise(config: ProblemConfig, stage: Stage) -> ProblemConfig: + """把阶段噪声写入 ProblemConfig（belief/PF 的保守裕量随之同步）。""" + field = dataclasses.replace( + config.field, bearing_error_half_deg=stage.bearing_error_half_deg) + return dataclasses.replace(config, field=field) + + +# 快速模式只减少选点评分的样本，不改变证明使用的幸存网格。29.3m 格距的 +# Voronoi 裕量约20.72m，已经超过20m清除半径，会使粒子证书永远不可达。 +# 默认使用生产配置；快速模式必须显式指定并在训练元数据记录。 +TRAINING_SCORE_SUBSAMPLE = 240 +TRAINING_MC_REPRESENTATIVES = 5 + + +def training_config(config: ProblemConfig, stage: Stage, *, + fidelity: str = "production") -> ProblemConfig: + """阶段噪声配置；fast只降低评分采样量，production与部署同参数。""" + if fidelity not in ("production", "fast"): + raise ValueError("fidelity 必须是 production 或 fast") + noisy = stage_with_noise(config, stage) + if fidelity == "production": + return noisy + bayes = dataclasses.replace( + noisy.bayes, + score_subsample=TRAINING_SCORE_SUBSAMPLE, + mc_representatives=TRAINING_MC_REPRESENTATIVES) + return dataclasses.replace(noisy, bayes=bayes) + + +def make_scene(rng: Random, config: ProblemConfig, problem: int, + stage: Stage, *, domain_randomization: bool = True + ) -> tuple[list, list[int]]: + """生成一局课程场景，返回 (referee.Source 列表, 已占用频道列表)。 + + 源位置在 Ω 圆域均匀分布、频道从 1..stage.channel_max 互异抽样、 + 有效接收半径 [1000,1500] 均匀 [题面]；问题4 按 stage.directional_ratio + 混入定向源，朝向均匀。domain_randomization=False 时只用 rng 的确定 + 序列（仍随机但可整场复现），供评估固定场景对照。 + """ + from referee import Source + + field = config.field + count = rng.randint(stage.sources_min, stage.sources_max) + channels = rng.sample(range(field.channel_min, stage.channel_max + 1), count) + sources = [] + for channel in channels: + radius = field.omega_radius_m * math.sqrt(rng.random()) + angle = rng.uniform(0.0, 2.0 * math.pi) + r_eff = rng.uniform(field.r_eff_min_m, field.r_eff_max_m) + directional = (problem == 4 + and rng.random() < stage.directional_ratio) + heading = rng.uniform(0.0, 360.0) if directional else 0.0 + if not domain_randomization: + # 固定化退耦：半径/朝向取区间代表值，保留位置随机（评估用）。 + r_eff = field.r_eff_min_m + sources.append(Source(channel=channel, x=radius * math.cos(angle), + y=radius * math.sin(angle), r_eff_m=r_eff, + directional=directional, heading_deg=heading)) + return sources, channels + + +def training_spec(config: ProblemConfig, stage: Stage, *, + max_real_duration_s: float = 3600.0): + """带阶段噪声的 referee Spec（训练侧使用；正式对接以 /enter 为准）。""" + from ..harness import referee_spec + return referee_spec(stage_with_noise(config, stage), + max_real_duration_s=max_real_duration_s) diff --git a/src/mcm26b/hrl/executor.py b/src/mcm26b/hrl/executor.py new file mode 100644 index 0000000..4728ef8 --- /dev/null +++ b/src/mcm26b/hrl/executor.py @@ -0,0 +1,113 @@ +"""低层到达执行器与观测：8 方向步进（prompt §4.2），训练/部署同源。 + +低层只负责"怎么去"：给定当前位置与子目标，输出离散 8 方向步进序列。 +步长每步 ≤ ``LOW_STEP_M``，近目标时缩短步长；到达阈值 ``LOW_REACH_M`` +与清除半径一致。推演完全本地，不与模拟器交互、不产生虚拟耗时——协议 +语义下移动耗时 = 两次动作位置间的直线距离 ÷ 速度，仅由终点决定。 + +本模块仅供独立导航实验和轨迹诊断；当前 HTTP 执行路径仍直接服务高层 +目标，不能据此声称低层 PPO 改善正式任务耗时。policy=None 使用解析 +直线控制器；PPO 推演超时不会免费跳到目标。 +""" + +from __future__ import annotations + +import math + +import numpy as np + +from ..config import Vec2 +from .constants import (LOW_MAX_STEPS, LOW_NUM_ACTIONS, LOW_OBS_DIM, + LOW_REACH_M, LOW_STEP_M, POS_SCALE_M) + +__all__ = ["build_low_observation", "LowLevelExecutor", "action_bearing", + "navigation_transition"] + + +def action_bearing(action: int) -> float: + """离散方向 -> 弧度方位（0 = +x，逆时针 45° 等分）。""" + return float(action) * 2.0 * math.pi / LOW_NUM_ACTIONS + + +def navigation_transition(position: Vec2, goal: Vec2, action: int, *, + step_m: float = LOW_STEP_M) -> tuple[Vec2, float]: + """训练与轨迹推演共享的运动学；返回位置和实际位移长度。 + + 方向仍由策略决定，近目标时仅缩短步长，不把位置吸附到目标。 + 这是无障碍平面导航实验；不引入观测不可见的绝对位置裁剪。 + """ + if not 0 <= int(action) < LOW_NUM_ACTIONS: + raise ValueError("低层动作必须是 0..7") + if not math.isfinite(step_m) or step_m <= 0: + raise ValueError("step_m 必须是有限正数") + distance = min(float(step_m), math.dist(position, goal)) + bearing = action_bearing(int(action)) + next_position = (position[0] + distance * math.cos(bearing), + position[1] + distance * math.sin(bearing)) + return next_position, math.dist(position, next_position) + + +def build_low_observation(position: Vec2, goal: Vec2, prev_action: int | None, + steps: int) -> np.ndarray: + """低层观测（shape=(LOW_OBS_DIM,)，训练/推理逐位一致）。""" + dx, dy = goal[0] - position[0], goal[1] - position[1] + distance = math.hypot(dx, dy) + bearing = math.atan2(dy, dx) + prev_sin = prev_cos = 0.0 + if prev_action is not None: + prev_bearing = action_bearing(prev_action) + prev_sin, prev_cos = math.sin(prev_bearing), math.cos(prev_bearing) + return np.asarray([ + dx / POS_SCALE_M, + dy / POS_SCALE_M, + distance / POS_SCALE_M, + math.sin(bearing), + math.cos(bearing), + steps / LOW_MAX_STEPS, + prev_sin, + prev_cos, + min(distance / LOW_STEP_M, 3.0) / 3.0, + 1.0, + ], dtype=np.float32) + + +class LowLevelExecutor: + """子目标到达推演：低层 PPO 或直线控制器（policy=None）。""" + + def __init__(self, policy=None, *, step_m: float = LOW_STEP_M, + reach_m: float = LOW_REACH_M, max_steps: int = LOW_MAX_STEPS): + self.policy = policy + self.step_m = float(step_m) + self.reach_m = float(reach_m) + self.max_steps = int(max_steps) + self.last_roll_info: dict = {} + + def roll(self, start: Vec2, goal: Vec2) -> list[Vec2]: + """返回真实推演途经点；last_roll_info 如实记录到达/截断。""" + goal = (float(goal[0]), float(goal[1])) + if self.policy is None: + self.last_roll_info = {"reached": True, "truncated": False, + "final_distance_m": 0.0, + "path_length_m": math.dist(start, goal)} + return [goal] + position = (float(start[0]), float(start[1])) + waypoints: list[Vec2] = [] + prev_action: int | None = None + path_length = 0.0 + for steps in range(self.max_steps): + if math.dist(position, goal) <= self.reach_m: + break + observation = build_low_observation(position, goal, prev_action, + steps) + action = int(self.policy.select(observation)) + position, moved = navigation_transition(position, goal, action, + step_m=self.step_m) + path_length += moved + waypoints.append(position) + prev_action = action + distance = math.dist(position, goal) + self.last_roll_info = {"reached": distance <= self.reach_m, + "truncated": distance > self.reach_m, + "final_distance_m": distance, + "path_length_m": path_length} + return waypoints diff --git a/src/mcm26b/hrl/hierarchical_env.py b/src/mcm26b/hrl/hierarchical_env.py new file mode 100644 index 0000000..38da088 --- /dev/null +++ b/src/mcm26b/hrl/hierarchical_env.py @@ -0,0 +1,261 @@ +"""高层 PPO 训练环境：整场 10–16 源的发现-定位-清除调度（prompt §2/§5.2）。 + +与 v2 :class:`~mcm26b.rl.env.LocalizationEnv`（单源 ≤16 步子任务）的本质 +差异：本环境的每一步是一次完整的高层调度决策（候选槽位选择 + 任务执行）， +环境语义与运行闭环 :class:`~mcm26b.planner.hrl_loop.HRLLoop` 共用同一 +混入层（hrl.scheduler），训练-部署观测逐位同源。 + +奖励（prompt §5.2 的可折算实现，权重见 :class:`HRLReward`）： + r = -Δ虚拟耗时/20 + 100·清除 + 5·新发现 + 0.5·Σ熵减 - 2·冗余检测 +终止：全部频道清除/证明缺席（+50）；截断仍有未决频道（-100）；异常（-100）。 +主口径是虚拟耗时——与题目目标函数（最小化平均定位清除时间/虚拟总时间） +一致，清除/发现/熵减是塑形项（消融实验逐项验证其必要性）。 + +真值只在 reset 布景时使用，绝不进入观测；感知读数全部来自 +referee.LocalSimulator（与官方 HTTP 模拟器同协议语义）。 +""" + +from __future__ import annotations + +from dataclasses import dataclass +from random import Random + +import gymnasium as gym +import numpy as np +from gymnasium import spaces + +from ..belief import ChannelStatus +from ..config import ProblemConfig +from ..hrl.curriculum import STAGES, Stage, make_scene, training_config +from ..hrl.constants import HIGH_OBS_DIM, N_CANDIDATES +from ..planner.hrl_loop import HRLLoop +from ..protocol.client import SimulatorClient +from ..protocol.log import SessionLog + +__all__ = ["HRLReward", "HierarchicalEnv"] + + +@dataclass +class HRLReward: + """高层奖励权重。 + + 默认保留 prompt §5.2 的可解释塑形，便于对照实验；P0.4 建议的 + 纯时间口径可通过 :meth:`time_only` 显式启用，训练脚本默认使用它。 + """ + + time_scale: float = 20.0 # r -= Δ虚拟耗时 / time_scale + clear: float = 100.0 # 每清除一个源 + detect: float = 5.0 # 每新发现一个频道有信号 + info_weight: float = 0.5 # 粒子云熵减（log 幸存格数）内在奖励 + redundant: float = 2.0 # 同频道同点重复检测惩罚 + invalid: float = 2.0 # 掩码外动作（无掩码消融路径） + terminal_accounted: float = 50.0 # 全部频道清除/证毕的收尾奖励 + terminal_unresolved: float = 100.0 # 截断/停机时仍有未决频道的惩罚 + failure: float = 100.0 # 协议/守恒性异常惩罚 + + @classmethod + def time_only(cls) -> "HRLReward": + """P0.4 纯时间目标：只惩罚已发生虚拟时间，去掉塑形项。 + + 终止奖/罚是固定常数，不逐源叠加；失败罚按虚拟时限/时间尺度 + 设定为足以覆盖一整局时间成本。 + """ + return cls( + time_scale=100.0, + clear=0.0, + detect=0.0, + info_weight=0.0, + redundant=0.0, + invalid=2.0, + terminal_accounted=30.0, + terminal_unresolved=300.0, + failure=3600.0, + ) + + +class HierarchicalEnv(gym.Env): + """整场分层调度 Gymnasium 环境（问题3/4，课程参数化）。 + + stage=None 时使用 STAGES 末阶段（完整任务口径）；课程训练按阶段 + 传入。domain_randomization=False 保留随机位置但固定半径代表值， + 供配对评估。 + """ + + metadata = {"render_modes": []} + + def __init__(self, config: ProblemConfig | None = None, *, + problem: int = 3, stage: Stage | None = None, + max_episode_steps: int = 300, + domain_randomization: bool = True, + reward: HRLReward | None = None, + real_budget_s: float = 3600.0, + error_seed: int | None = None, + fidelity: str = "production"): + super().__init__() + if problem not in (3, 4): + raise ValueError("problem 必须是 3 或 4") + self.base_config = config or ProblemConfig() + self.problem = int(problem) + self.stage = stage or STAGES[-1] + self.max_episode_steps = int(max_episode_steps) + self.domain_randomization = bool(domain_randomization) + self.reward = reward or HRLReward() + self.real_budget_s = float(real_budget_s) + self.error_seed = error_seed + self.fidelity = fidelity + # 默认与部署同精度；fast仅减少评分采样，保留证书网格精度。 + self.train_config = training_config(self.base_config, self.stage, + fidelity=fidelity) + self.observation_space = spaces.Box(low=-12.0, high=12.0, + shape=(HIGH_OBS_DIM,), + dtype=np.float32) + self.action_space = spaces.Discrete(N_CANDIDATES) + + self._scene_rng = Random() + self._loop: HRLLoop | None = None + self._client: SimulatorClient | None = None + self._steps = 0 + self._terminated = False + self._truncated = False + self._start_virtual_s = 0.0 + self._current_candidates: list = [] + self._failure_reason: str | None = None + self._last_outcome = None + + # ------------------------------------------------------------ 场景 + def _make_episode(self) -> None: + from ..harness import LocalSimTransport + from referee import LocalSimulator + + sources, _channels = make_scene( + self._scene_rng, self.train_config, self.problem, self.stage, + domain_randomization=self.domain_randomization) + self._simulator = LocalSimulator( + sources, robot_id="hrl-train", + spec=self._training_spec(), + error_seed=(self.error_seed if self.error_seed is not None + else self._scene_rng.randint(0, 2 ** 31 - 1)), + stress_mode=True) + self._client = SimulatorClient( + "hrl-train", config=self.train_config, + transport=LocalSimTransport(self._simulator), log=SessionLog()) + self._client.enter() + self._loop = HRLLoop(self._client, self.train_config, + problem=self.problem) + + def _training_spec(self): + from ..harness import referee_spec + from ..hrl.curriculum import training_spec + return training_spec(self.train_config, self.stage, + max_real_duration_s=self.real_budget_s) + + # ------------------------------------------------------------ Gym API + def reset(self, *, seed: int | None = None, options: dict | None = None): + super().reset(seed=seed) + if seed is not None: + self._scene_rng = Random(seed) + self._steps = 0 + self._terminated = False + self._truncated = False + self._failure_reason = None + self._make_episode() + self._start_virtual_s = self._client.state.virtual_time_s + self._current_candidates = self._loop.build_candidates() + observation = self._loop.hrl_observation(self._current_candidates) + return observation, self._info() + + def action_masks(self) -> np.ndarray: + """MaskablePPO 动作掩码接口（与观测同源的候选槽位有效性）。""" + return self._loop.hrl_action_mask(self._current_candidates) + + def _info(self) -> dict: + loop = self._loop + belief = loop.belief + cleared = len(loop.cleared) + absent = sum(1 for ch in belief.channels.values() + if ch.status is ChannelStatus.ABSENT) + return { + "steps": self._steps, + "cleared": cleared, + "absent_proven": absent, + "accounted": cleared + absent, + "virtual_time_s": self._client.state.virtual_time_s, + "episode_virtual_cost_s": (self._client.state.virtual_time_s + - self._start_virtual_s), + "stop_reason": loop.stop_reason, + "failure_reason": self._failure_reason, + "anomalies": list(loop.anomalies), + } + + def step(self, action): + if self._terminated or self._truncated: + raise RuntimeError("step() 需要在 terminated/truncated 后先 reset()") + loop = self._loop + self._steps += 1 + try: + outcome = loop.hrl_step(action, candidates=self._current_candidates) + except Exception as exc: # 协议/守恒性异常：与 v2 环境同口径收尾 + self._failure_reason = type(exc).__name__ + ': ' + str(exc) + observation = np.zeros((HIGH_OBS_DIM,), dtype=np.float32) + self._terminated = True + return (observation, -self.reward.failure, True, False, + self._info()) + self._last_outcome = outcome + + if outcome.invalid: + reward = -self.reward.invalid + else: + reward = self._reward_from(outcome) + + # 终止判定：停机原因/全部证毕；或候选耗尽时走运行闭环 run() 的 + # 同款安全网（覆盖缺席标记 + 阻塞定位清除），安全网的虚拟耗时 + # 计入本步奖励（归因给促成该局面的决策序列）。 + terminated = loop.stop_reason is not None or loop._all_accounted() + if not terminated: + self._current_candidates = loop.build_candidates() + if not self._current_candidates: + virtual_before = self._client.state.virtual_time_s + loop._mark_absent_after_coverage() + loop._resolve_detected() + reward -= (self._client.state.virtual_time_s - virtual_before) \ + / self.reward.time_scale + self._current_candidates = [] + terminated = True + else: + self._current_candidates = [] + + if terminated: + reward += (self.reward.terminal_accounted + if loop._all_accounted() + else -self.reward.terminal_unresolved) + truncated = False + if not terminated and self._steps >= self.max_episode_steps: + truncated = True + if not loop._all_accounted(): + reward -= self.reward.terminal_unresolved + self._terminated = terminated + self._truncated = truncated + if terminated or truncated: + observation = np.zeros((HIGH_OBS_DIM,), dtype=np.float32) + else: + self._current_candidates = loop.build_candidates() + observation = loop.hrl_observation(self._current_candidates) + return observation, float(reward), terminated, truncated, self._info() + + def _reward_from(self, outcome) -> float: + reward = -outcome.delta_virtual_s / self.reward.time_scale + reward += self.reward.clear * len(outcome.clear_success) + reward += self.reward.detect * len(outcome.new_detections) + reward += self.reward.info_weight * sum(outcome.info_gains.values()) + if outcome.redundant: + reward -= self.reward.redundant + return reward + + def close(self): + client = getattr(self, "_client", None) + if client is not None and client.state.entered \ + and not client.state.exited: + try: + client.exit() + except Exception: + pass diff --git a/src/mcm26b/hrl/low_level_env.py b/src/mcm26b/hrl/low_level_env.py new file mode 100644 index 0000000..3c1d236 --- /dev/null +++ b/src/mcm26b/hrl/low_level_env.py @@ -0,0 +1,158 @@ +"""低层 PPO 训练环境：随机起点-子目标的 8 方向到达任务（prompt §4.2/§5.1）。 + +奖励为实际位移耗时惩罚、转向惩罚与到达奖励。步长上限 100m，近目标 +缩短步长，避免固定步长振荡。此环境仅用于独立导航实验；正式 HTTP +路径尚不执行低层 PPO 的途经点，不能将导航成绩当成正式任务收益。 + +策略端只见相对位姿观测，真值（子目标坐标）经相对向量进入观测， +与部署执行器完全同源（build_low_observation）。 +""" + +from __future__ import annotations + +import math +from random import Random + +import gymnasium as gym +import numpy as np +from gymnasium import spaces + +from ..config import ProblemConfig +from .constants import (LOW_MAX_STEPS, LOW_NUM_ACTIONS, LOW_OBS_DIM, + LOW_REACH_M, LOW_STEP_M, POS_SCALE_M) +from .executor import (action_bearing, build_low_observation, + navigation_transition) + +__all__ = ["LowLevelEnv"] + + +class LowLevelEnv(gym.Env): + """低层到达任务：观测 10 维，动作 Discrete(8)，逐步计费。""" + + metadata = {"render_modes": []} + + def __init__(self, config: ProblemConfig | None = None, *, + seed: int | None = None, + step_m: float = LOW_STEP_M, reach_m: float = LOW_REACH_M, + max_steps: int = LOW_MAX_STEPS, + min_distance_m: float = 300.0, + turn_penalty: float = 0.05, reach_bonus: float = 2.0, + step_cost_scale: float = 100.0, + truncation_penalty: float = 2.0): + super().__init__() + self.config = config or ProblemConfig() + self.step_m = float(step_m) + self.reach_m = float(reach_m) + self.max_steps = int(max_steps) + self.min_distance_m = float(min_distance_m) + self.turn_penalty = float(turn_penalty) + self.reach_bonus = float(reach_bonus) + self.step_cost_scale = float(step_cost_scale) + self.truncation_penalty = float(truncation_penalty) + self.observation_space = spaces.Box(low=-np.inf, high=np.inf, + shape=(LOW_OBS_DIM,), + dtype=np.float32) + self.action_space = spaces.Discrete(LOW_NUM_ACTIONS) + self._rng = Random(seed) + self._position = (0.0, 0.0) + self._goal = (0.0, 0.0) + self._steps = 0 + self._prev_action: int | None = None + self._terminated = False + self._truncated = False + self._path_length_m = 0.0 + self._virtual_time_s = 0.0 + self._straight_m = 0.0 + + # ------------------------------------------------------------ Gym API + def reset(self, *, seed: int | None = None, options: dict | None = None): + super().reset(seed=seed) + if seed is not None: + self._rng = Random(seed) + radius = self.config.field.omega_radius_m + self._position = self._sample_point(radius) + for _ in range(100): + self._goal = self._sample_point(radius) + if math.dist(self._position, self._goal) >= self.min_distance_m: + break + self._steps = 0 + self._prev_action = None + self._terminated = False + self._truncated = False + self._path_length_m = 0.0 + self._virtual_time_s = 0.0 + self._straight_m = math.dist(self._position, self._goal) + return self._observe(), self._info() + + def action_masks(self) -> np.ndarray: + """无障碍低层八个方向均可用，兼容 MaskablePPO。""" + return np.ones(LOW_NUM_ACTIONS, dtype=bool) + + def _sample_point(self, radius: float) -> tuple[float, float]: + angle = self._rng.uniform(0.0, 2.0 * math.pi) + distance = radius * math.sqrt(self._rng.random()) + return (distance * math.cos(angle), distance * math.sin(angle)) + + def step(self, action): + if self._terminated or self._truncated: + raise RuntimeError("step() 需要在 terminated/truncated 后先 reset()") + action = int(action) + if self._prev_action is not None: + turn = abs((action_bearing(action) + - action_bearing(self._prev_action) + math.pi) + % (2.0 * math.pi) - math.pi) / math.pi + else: + turn = 0.0 + self._position, moved = navigation_transition( + self._position, self._goal, action, step_m=self.step_m) + self._prev_action = action + self._steps += 1 + + step_cost_s = moved / self.config.cost.speed_mps + self._path_length_m += moved + self._virtual_time_s += step_cost_s + reward = -step_cost_s / self.step_cost_scale + reward -= self.turn_penalty * turn + distance = math.dist(self._position, self._goal) + terminated = distance <= self.reach_m + truncated = False + if terminated: + reward += self.reach_bonus + elif self._steps >= self.max_steps: + truncated = True + reward -= self.truncation_penalty + self._terminated = terminated + self._truncated = truncated + info = self._info() + info["final_distance_m"] = distance + info["step_distance_m"] = moved + info["step_virtual_time_s"] = step_cost_s + return self._observe(), float(reward), terminated, truncated, info + + def _observe(self) -> np.ndarray: + return build_low_observation(self._position, self._goal, + self._prev_action, self._steps) + + def _info(self) -> dict: + return { + "position": self._position, + "goal": self._goal, + "steps": self._steps, + "distance_m": math.dist(self._position, self._goal), + "straight_m": self._straight_m, + "path_length_m": self._path_length_m, + "virtual_time_s": self._virtual_time_s, + } + + # ------------------------------------------------------------ 专家（BC） + def expert_action(self) -> int: + """直线专家：选择方位最接近子目标方向的离散方向（BC 标签）。""" + bearing = math.atan2(self._goal[1] - self._position[1], + self._goal[0] - self._position[0]) + best, best_gap = 0, float("inf") + for action in range(LOW_NUM_ACTIONS): + gap = abs((action_bearing(action) - bearing + math.pi) + % (2.0 * math.pi) - math.pi) + if gap < best_gap: + best, best_gap = action, gap + return best diff --git a/src/mcm26b/hrl/policy.py b/src/mcm26b/hrl/policy.py new file mode 100644 index 0000000..086fada --- /dev/null +++ b/src/mcm26b/hrl/policy.py @@ -0,0 +1,135 @@ +"""HRL 策略加载与推理（运行时接入 planner 的唯一入口，模式同 rl.policy）。 + +模型文件由 scripts/train_hrl_low.py / train_hrl_high.py 生成，并伴随同名 +JSON 元数据（feature_version/algorithm/problem/维度）；加载时校验，失配 +即拒绝运行，杜绝静默错位。stable-baselines3 / torch 均为惰性导入。 +""" + +from __future__ import annotations + +import json +from pathlib import Path + +import numpy as np + +from .constants import (ACTION_LAYOUT_VERSION, FEATURE_VERSION, + HIGH_OBS_DIM, LOW_NUM_ACTIONS, LOW_OBS_DIM, + N_CANDIDATES, REWARD_VERSION) + +__all__ = ["HighLevelPolicy", "LowLevelPolicy", + "default_high_model_path", "default_low_model_path"] + + +def _resolve_device(device: str) -> str: + if device != "auto": + return device + try: + import torch + return "cuda" if torch.cuda.is_available() else "cpu" + except ImportError: + return "cpu" + + +def _load_maskable(model_path: Path, device: str, *, + obs_dim: int, act_dim: int, problem: int): + if not model_path.is_file(): + raise RuntimeError( + f"未找到 HRL 模型 {model_path}。请先训练：\n" + f" uv sync --group rl --group dev\n" + f" uv run python scripts/train_hrl_high.py --problem {problem}") + try: + from sb3_contrib import MaskablePPO + except ImportError as exc: + raise RuntimeError( + "缺少 RL 运行依赖（sb3-contrib）。请执行 `uv sync --group rl` " + "后重试。") from exc + meta_path = model_path.with_suffix('.json') + metadata = json.loads(meta_path.read_text(encoding='utf-8')) \ + if meta_path.exists() else {} + # 高层模型必须携带完整语义元数据；缺字段不能默认兼容，防止旧槽位/ + # 旧奖励语义模型被静默加载。低层导航模型只校验基础特征版本。 + strict = (act_dim == N_CANDIDATES) + if strict: + required = ("feature_version", "action_layout_version", + "reward_version", "algorithm", "problem", + "obs_dim", "act_dim") + missing = [key for key in required if key not in metadata] + if missing: + raise RuntimeError( + f"HRL 高层模型元数据缺少字段 {missing}；请用当前 " + f"scripts/train_hrl_high.py 重训。") + if metadata.get("feature_version") != FEATURE_VERSION: + raise RuntimeError( + f"HRL 模型特征版本失配: 期望 {FEATURE_VERSION}，" + f"实际 {metadata.get('feature_version')}。请用当前版本重训。") + if metadata.get("action_layout_version") != ACTION_LAYOUT_VERSION: + raise RuntimeError( + f"HRL 模型动作布局版本失配: 期望 {ACTION_LAYOUT_VERSION}，" + f"实际 {metadata.get('action_layout_version')}。请重训。") + if metadata.get("reward_version") != REWARD_VERSION: + raise RuntimeError( + f"HRL 模型奖励版本失配: 期望 {REWARD_VERSION}，" + f"实际 {metadata.get('reward_version')}。请重训。") + else: + if metadata.get("feature_version") != FEATURE_VERSION: + raise RuntimeError( + f"低层模型特征版本失配: 期望 {FEATURE_VERSION}，" + f"实际 {metadata.get('feature_version')}。请重训。") + if metadata.get("algorithm") != "maskable_ppo": + raise RuntimeError("HRL 模型必须是 maskable_ppo 训练产物") + if problem is not None and metadata.get("problem") not in (None, problem): + raise RuntimeError("模型训练问题编号与当前第三/四问不匹配") + model = MaskablePPO.load(str(model_path), device=device) + try: + loaded_obs = model.observation_space.shape[0] + loaded_act = model.action_space.n + except Exception as exc: + raise RuntimeError(f"模型空间与 HRL 接口不兼容: {model_path}") from exc + if loaded_obs != obs_dim or loaded_act != act_dim: + raise RuntimeError( + f"模型接口失配: 期望 obs={obs_dim}/act={act_dim}，" + f"实际 obs={loaded_obs}/act={loaded_act}。请重训。") + return model + + +class HighLevelPolicy: + """高层子目标调度策略：观测+掩码 -> 槽位编号（确定性 argmax）。""" + + def __init__(self, model_path: str | Path, *, device: str = "auto", + problem: int | None = None): + self.device = _resolve_device(device) + self.path = Path(model_path) + self.model = _load_maskable(self.path, self.device, obs_dim=HIGH_OBS_DIM, + act_dim=N_CANDIDATES, problem=problem) + meta_path = self.path.with_suffix('.json') + self.metadata = json.loads(meta_path.read_text(encoding='utf-8')) \ + if meta_path.exists() else {} + + def select(self, observation: np.ndarray, action_masks: np.ndarray) -> int: + action, _state = self.model.predict( + observation, action_masks=action_masks, deterministic=True) + return int(action) + + +class LowLevelPolicy: + """低层到达策略：相对位姿观测 -> 8 方向（确定性 argmax）。""" + + def __init__(self, model_path: str | Path, *, device: str = "auto"): + self.device = _resolve_device(device) + self.path = Path(model_path) + self.model = _load_maskable(self.path, self.device, obs_dim=LOW_OBS_DIM, + act_dim=LOW_NUM_ACTIONS, problem=None) + + def select(self, observation: np.ndarray) -> int: + action, _state = self.model.predict(observation, deterministic=True) + return int(action) + + +def default_high_model_path(problem: int) -> Path: + if problem not in (3, 4): + raise ValueError("problem 必须是 3 或 4") + return Path("output/hrl") / f"q{problem}_hrl_high.zip" + + +def default_low_model_path() -> Path: + return Path("output/hrl") / "hrl_low.zip" diff --git a/src/mcm26b/hrl/scheduler.py b/src/mcm26b/hrl/scheduler.py new file mode 100644 index 0000000..34c45ca --- /dev/null +++ b/src/mcm26b/hrl/scheduler.py @@ -0,0 +1,493 @@ +"""高层调度核心：候选槽位构建、观测、动作掩码与任务执行。 + +本模块是 :class:`~mcm26b.planner.hrl_loop.HRLLoop`（运行闭环）与 +:class:`~mcm26b.hrl.hierarchical_env.HierarchicalEnv`（训练环境）共用的 +混入层，保证训练-部署特征逐位同源（v1"训练部署特征错位"审核教训）。 + +宿主须为 :class:`~mcm26b.planner.bayes_loop.BayesLoop` 派生闭环，提供 +client/belief/particles/ledger/_record_measure 等感知与执行原语；本层只做 +三件事—— + +1. 把整场调度决策离散化为固定槽位（constants.N_CANDIDATES）： + clear(20) + locate(20) + station(49) + fallback(20) + 保留(11)； +2. 由可见观测（belief + 网格幸存集 + 覆盖账本）构造观测与动作掩码， + 绝不读取源真值； +3. 执行选中候选并报告事实（虚拟耗时增量、新发现、熵减、清除结果）， + 奖励折算由训练环境负责。 + +Q4 定向源语义：no_signal 不能删除空间候选（可能是朝向遮挡），故问题4 的 +幸存集只按 direction/near 更新；遮挡证据走 blocked_probes 梯子与后备清除 +槽位，与 DirectionalExtension 的保守口径一致。 +""" + +from __future__ import annotations + +import math +from dataclasses import dataclass, field +from typing import NamedTuple + +import numpy as np + +from ..belief import ChannelStatus +from ..config import ProblemConfig, Vec2 +from ..protocol.rules import CLEAR_SUCCESS, DIRECTION, NEAR, NO_SIGNAL +from ..protocol.timing import move_time +from .constants import (CHANNEL_COUNT, CLEAR_OFFSET, COST_SCALE_S, + FALLBACK_OFFSET, HIGH_GLOBAL_DIM, HIGH_OBS_DIM, + LOCATE_OFFSET, MEASURE_COUNT_SCALE, N_CANDIDATES, + PER_CANDIDATE_DIM, PER_CHANNEL_DIM, POS_SCALE_M, + RADIUS_SCALE_M, STATION_OFFSET) +from ..planner import fallback_clear_cells, safe_disk_radius + +__all__ = ["Candidate", "StepOutcome", "HRLControllerMixin", "point_key"] + + +def point_key(point: Vec2) -> tuple[int, int]: + """候选点去重键：0.5m 网格取整（与 bayes_loop 同口径）。""" + return (int(round(point[0] * 2)), int(round(point[1] * 2))) + + +class Candidate(NamedTuple): + """一个高层候选子目标。slot 为固定槽位编号，供动作掩码与特征定位。""" + + slot: int + kind: str # "clear" | "locate" | "probe" | "station" | "fallback" + position: Vec2 + channel: int | None = None + station_index: int | None = None + service_s: float = 0.0 # 预计服务耗时（含换频，不含移动） + targets: int = 1 # station: 待测频道数；fallback: 格数；其余 1 + + +@dataclass +class StepOutcome: + """一次高层决策-执行的客观结果（奖励折算由训练环境负责）。""" + + action: int + kind: str | None = None + channel: int | None = None + invalid: bool = False # 掩码外动作（无掩码消融路径）：未与模拟器交互 + delta_virtual_s: float = 0.0 + new_detections: tuple[int, ...] = () + info_gains: dict[int, float] = field(default_factory=dict) # 频道 -> 熵减(log) + clear_success: tuple[int, ...] = () + redundant: bool = False + position_reached: Vec2 | None = None + + +class HRLControllerMixin: + """高层调度混入：依赖宿主（HRLLoop）提供 BayesLoop 感知与执行原语。 + + 宿主须在 ``__init__`` 初始化：``_obs_mec_cache={}``、 + ``_station_pending_map={}``、``_initial_alive={channel: len(pf)}`` + （观测侧缓存与归一化基准；证书相关计算仍用 pf.mec() 精确值）。 + """ + + problem: int + + # ------------------------------------------------------------ 候选构建 + def build_candidates(self) -> list[Candidate]: + """构建当前全部有效候选（固定槽位；训练/推理/heuristic 专家同源）。 + + 副作用：缓存每频道证书（``self._cert_cache``）与站点待测集 + （``self._station_pending_map``）供观测构建复用；调用方须在本 + 函数之后、任何状态变更之前构建观测。 + """ + config = self.config + detected = self.belief.channels_by_status(ChannelStatus.DETECTED) + by_slot: dict[int, Candidate] = {} + cert_cache: dict[int, object] = {} + station_map: dict[int, list[int]] = {} + + # --- clear 槽位：证书就绪且未因失败等待补测 + for channel in detected: + cert = self._best_certificate(channel) + cert_cache[channel] = cert + if cert is not None and cert.certified \ + and channel not in self._clear_failed: + by_slot[CLEAR_OFFSET + channel - 1] = Candidate( + CLEAR_OFFSET + channel - 1, "clear", tuple(cert.center), + channel=channel, + service_s=config.cost.clear_success_s, targets=1) + + # --- locate / probe 槽位：未就绪清除的已发现频道 + for channel in detected: + if CLEAR_OFFSET + channel - 1 in by_slot: + continue + region = self.belief.candidate_set(channel) + if region is None: + continue + blocked = self.blocked_probes.get(channel, []) \ + if self.problem == 4 else [] + if blocked: + point = self._hrl_probe_point(region, channel) + kind = "probe" + else: + point = self._select_point(channel) + kind = "locate" + if point is None: + continue + by_slot[LOCATE_OFFSET + channel - 1] = Candidate( + LOCATE_OFFSET + channel - 1, kind, tuple(point), channel=channel, + service_s=config.cost.measure_s + config.cost.switch_s, targets=1) + + # --- station 槽位：尚有 UNKNOWN 待测频道的覆盖站 + for index, point in enumerate(self.ledger.points): + pending = self._station_pending(index) + station_map[index] = pending + if not pending: + continue + switches = len(pending) - (1 if self.client.state.channel in pending else 0) + by_slot[STATION_OFFSET + index] = Candidate( + STATION_OFFSET + index, "station", tuple(point), + station_index=index, + service_s=(config.cost.measure_s * len(pending) + + config.cost.switch_s * max(switches, 0)), + targets=len(pending)) + + # --- fallback 槽位（仅问题4）：遮挡梯子耗尽且格数在预算内 + if self.problem == 4: + for channel in detected: + blocked = self.blocked_probes.get(channel, []) + if len(blocked) < config.search.max_blocked_probes: + continue + region = self.belief.candidate_set(channel) + if region is None: + continue + cells = fallback_clear_cells(region.polygon, config) + if not cells or len(cells) > config.search.fallback_max_cells: + continue + by_slot[FALLBACK_OFFSET + channel - 1] = Candidate( + FALLBACK_OFFSET + channel - 1, "fallback", + tuple(region.mec()[0]), channel=channel, + service_s=config.cost.clear_fail_s * len(cells), + targets=len(cells)) + + self._cert_cache = cert_cache + self._station_pending_map = station_map + self._last_candidates = list(by_slot.values()) + return self._last_candidates + + def candidate_by_slot(self, candidates: list[Candidate], + action: int) -> Candidate | None: + for cand in candidates: + if cand.slot == int(action): + return cand + return None + + # ------------------------------------------------------------ 动作掩码 + def hrl_action_mask(self, candidates: list[Candidate]) -> np.ndarray: + """槽位有效性掩码：无候选槽位恒屏蔽（训练/推理共用）。""" + mask = np.zeros(N_CANDIDATES, dtype=bool) + for cand in candidates: + mask[cand.slot] = True + return mask + + # ------------------------------------------------------------ 观测 + def _hrl_obs_mec(self, channel: int) -> tuple[Vec2, float]: + """观测用 MEC：幸存子采样上的最小包围圆，按 pf.version 缓存。 + + 仅作策略观测特征（近似 MEU/σ），证书相关计算一律用 pf.mec() + 精确值——两者分开缓存，互不影响。 + """ + pf = self.particles[channel] + cached = self._obs_mec_cache.get(channel) + if cached is not None and cached[0] == pf.version: + return cached[1], cached[2] + points = pf.subsample(600) + if not points: + return (0.0, 0.0), 0.0 + from ..geometry.localization import smallest_enclosing_circle + center, radius = smallest_enclosing_circle(points) + self._obs_mec_cache[channel] = (pf.version, center, radius) + return center, radius + + def _hrl_channel_status_index(self, channel: int) -> int: + """六状态编码：UNKNOWN/DETECTED/TRACKING/LOCATED/CLEARED/ABSENT。""" + status = self.belief.channels[channel].status + if status is ChannelStatus.UNKNOWN: + return 0 + if status is ChannelStatus.CLEARED: + return 4 + if status is ChannelStatus.ABSENT: + return 5 + cert = getattr(self, "_cert_cache", {}).get(channel) + if cert is not None and getattr(cert, "certified", False): + return 3 + _, radius = self._hrl_obs_mec(channel) + return 1 if radius > 500.0 else 2 + + def hrl_observation(self, candidates: list[Candidate]) -> np.ndarray: + """构造高层观测（shape=(HIGH_OBS_DIM,)，训练/推理逐位一致）。""" + config = self.config + field = config.field + position = self.client.state.position + belief = self.belief + + detected_count = len(belief.channels_by_status(ChannelStatus.DETECTED)) + accounted = sum(1 for ch in belief.channels.values() + if ch.status in (ChannelStatus.CLEARED, + ChannelStatus.ABSENT)) + cleared = len(self.cleared) + glob = [ + position[0] / POS_SCALE_M, + position[1] / POS_SCALE_M, + min(self.client.state.virtual_time_s, field.max_virtual_duration_s) + / field.max_virtual_duration_s, + cleared / 16.0, + accounted / CHANNEL_COUNT, + (self.client.state.channel - field.channel_min) + / max(field.channel_max - field.channel_min, 1), + float(self.problem == 4), + detected_count / 16.0, + ] + assert len(glob) == HIGH_GLOBAL_DIM + + pending_station = np.zeros(CHANNEL_COUNT + 1, dtype=bool) + for index, channels in getattr(self, "_station_pending_map", {}).items(): + for channel in channels: + pending_station[channel] = True + + channel_features: list[float] = [] + for channel in range(field.channel_min, field.channel_max + 1): + ch = belief.channels[channel] + status6 = [0.0] * 6 + status6[self._hrl_channel_status_index(channel)] = 1.0 + pf = self.particles[channel] + mec_visible = ch.status is ChannelStatus.DETECTED + if mec_visible: + center, radius = self._hrl_obs_mec(channel) + else: + center, radius = (0.0, 0.0), 0.0 + initial = max(self._initial_alive.get(channel, 1), 1) + channel_features.extend([ + *status6, + center[0] / POS_SCALE_M, + center[1] / POS_SCALE_M, + radius / RADIUS_SCALE_M, + math.log(len(pf) + 1) / math.log(initial + 1) + if initial > 1 else 0.0, + min(len(ch.measurements), MEASURE_COUNT_SCALE * 2) + / MEASURE_COUNT_SCALE / 2, + math.hypot(center[0] - position[0], center[1] - position[1]) + / POS_SCALE_M if mec_visible else 0.0, + len(self.blocked_probes.get(channel, [])) + / max(config.search.max_blocked_probes, 1) + if self.problem == 4 else 0.0, + 1.0 if (ch.status is ChannelStatus.UNKNOWN + and pending_station[channel]) else 0.0, + ]) + assert len(channel_features) == CHANNEL_COUNT * PER_CHANNEL_DIM + + by_slot = {cand.slot: cand for cand in candidates} + candidate_features: list[float] = [] + for slot in range(N_CANDIDATES): + cand = by_slot.get(slot) + if cand is None: + candidate_features.extend([0.0] * PER_CANDIDATE_DIM) + continue + dx, dy = cand.position[0] - position[0], cand.position[1] - position[1] + move_s = move_time(position, cand.position, config.cost) + candidate_features.extend([ + 1.0 if cand.kind == "clear" else 0.0, + 1.0 if cand.kind in ("locate", "probe") else 0.0, + 1.0 if cand.kind == "station" else 0.0, + 1.0 if cand.kind == "fallback" else 0.0, + dx / POS_SCALE_M, + dy / POS_SCALE_M, + min((move_s + cand.service_s) / COST_SCALE_S, 2.0), + min(cand.targets / 16.0, 1.0), + ]) + assert len(candidate_features) == N_CANDIDATES * PER_CANDIDATE_DIM + + observation = np.asarray(glob + channel_features + candidate_features, + dtype=np.float32) + assert observation.shape == (HIGH_OBS_DIM,) + return observation + + # ------------------------------------------------------------ 启发式专家 + def heuristic_action(self, candidates: list[Candidate]) -> int: + """把 BayesLoop 任务排序映射为槽位（BC 专家 + 无策略降级路径）。 + + 任务表与排序完全复用 :meth:`BayesLoop._build_tasks` / + :meth:`BayesLoop._order_tasks`；映射失败（如 Q4 梯子任务不在 + 任务表中）退化为最近有效候选。 + """ + by_slot = {cand.slot: cand for cand in candidates} + if not by_slot: + raise RuntimeError("heuristic_action：当前无可用候选") + try: + order = self._order_tasks(self._build_tasks()) + first = order[0] + if first.kind == "clear" and first.channel is not None: + slot = CLEAR_OFFSET + first.channel - 1 + elif first.kind == "locate" and first.channel is not None: + slot = LOCATE_OFFSET + first.channel - 1 + else: + slot = STATION_OFFSET + (first.station_index or 0) + if slot in by_slot: + return slot + except Exception: + pass + position = self.client.state.position + return min(by_slot, key=lambda s: ( + math.dist(position, by_slot[s].position), s)) + + # ------------------------------------------------------------ 决策执行 + def hrl_step(self, action: int, + candidates: list[Candidate] | None = None) -> StepOutcome: + """执行一次高层决策；candidates 为空则现场构建（训练环境逐位同源）。""" + action = int(action) + self._reset_step_recording() + old_virtual = self.client.state.virtual_time_s + if candidates is None: + candidates = self.build_candidates() + cand = self.candidate_by_slot(candidates, action) + if cand is None: + return StepOutcome(action=action, invalid=True) + if self.stop_reason is not None: + return StepOutcome(action=action, kind=cand.kind, + channel=cand.channel, invalid=True) + + if cand.kind == "station": + self._execute_station(cand.station_index) + elif cand.kind in ("locate", "probe"): + self._hrl_execute_locate(cand) + elif cand.kind == "clear": + self._hrl_execute_clear(cand) + elif cand.kind == "fallback": + self._hrl_execute_fallback(cand) + else: # pragma: no cover - 布局守卫 + raise ValueError(f"未知候选类型: {cand.kind}") + + outcome = StepOutcome(action=action, kind=cand.kind, + channel=cand.channel, + position_reached=tuple(self.client.state.position)) + outcome.delta_virtual_s = (self.client.state.virtual_time_s - old_virtual) + new_detections: list[int] = [] + info_gains: dict[int, float] = {} + redundant = False + for event in self._step_events: + if event["was_unknown"] and event["result"] in (DIRECTION, NEAR): + new_detections.append(event["channel"]) + gain = math.log10(event["pf_before"] + 1) - math.log10(event["pf_after"] + 1) + info_gains[event["channel"]] = (info_gains.get(event["channel"], 0.0) + + min(max(gain, 0.0), 2.0)) + redundant = redundant or event["redundant"] + outcome.new_detections = tuple(new_detections) + outcome.info_gains = info_gains + outcome.redundant = redundant + outcome.clear_success = tuple(self._step_clears) + self._step_clears = [] + return outcome + + # ------------------------------------------------------------ 执行原语 + def _hrl_execute_locate(self, cand: Candidate) -> None: + """定位测量（含 Q4 梯子探点）：记录证据，不自动清除（高层决策）。""" + config = self.config + channel = cand.channel + pf = self.particles[channel] + before = len(pf) + outcome = self.client.measure(cand.position, channel) + self._record_measure(channel, cand.position, outcome) + self._may_clear.add(channel) + if self.problem == 4: + if outcome.result == NO_SIGNAL: + if channel not in self.directional_observed: + region = self.belief.candidate_set(channel) + safe = safe_disk_radius(region, config) if region else -1.0 + if safe >= 0 and math.dist(cand.position, region.mec()[0]) <= safe: + self.directional_observed.append(channel) + self.blocked_probes.setdefault(channel, []).append(cand.position) + else: + self.blocked_probes.pop(channel, None) + if len(pf) == before: + self._stall[channel] = self._stall.get(channel, 0) + 1 + self.no_shrink_measures += 1 + else: + self._stall[channel] = 0 + + def _hrl_execute_clear(self, cand: Candidate) -> None: + """证书清除：证书失效（不应被掩码允许）时静默跳过。""" + cert = self._best_certificate(cand.channel) + if cert is None or not cert.certified: + self._anomaly("hrl_clear_without_certificate", cand.channel) + return + if self._attempt_clear(cand.channel, cert.center, cert): + self._clear_failed.discard(cand.channel) + self._stall.pop(cand.channel, None) + else: + self._clear_failed.add(cand.channel) + self._may_clear.discard(cand.channel) + + def _hrl_execute_fallback(self, cand: Candidate) -> None: + """Q4 后备网格清除（与 DirectionalExtension._fallback_clear 同口径）。""" + channel = cand.channel + region = self.belief.candidate_set(channel) + if region is None: + return + config, session = self.config, self.config.session + cells = fallback_clear_cells(region.polygon, config) + entry = {"channel": channel, "cells": len(cells)} + self.fallback_log.append(entry) + if len(cells) > config.search.fallback_max_cells: + entry["outcome"] = "skipped_cell_cap" + return + remaining = self.client.real_remaining_s + if remaining is not None and len(cells) * session.real_rtt_p95_s \ + + session.reserve_exit_s > remaining: + entry["outcome"] = "skipped_budget" + return + remaining_cells = list(cells) + attempts = 0 + while remaining_cells: + if self._virtual_cap_reached(): + entry["outcome"] = "skipped_virtual_budget" + entry["attempts"] = attempts + return + cell = min(remaining_cells, key=lambda c: ( + math.dist(self.client.state.position, c), c)) + remaining_cells.remove(cell) + attempts += 1 + outcome = self.client.clear(cell, channel) + if outcome.result == CLEAR_SUCCESS: + self.belief.mark_cleared(channel) + self.cleared.append(channel) + self._step_clears.append(channel) + self.blocked_probes.pop(channel, None) + entry["outcome"] = "cleared" + entry["attempts"] = attempts + return + entry["outcome"] = "exhausted" + entry["attempts"] = attempts + self._anomaly("hrl_fallback_exhausted", channel, cells=len(cells)) + + def _hrl_probe_point(self, region, channel: int) -> Vec2: + """定向遮挡梯子探点（与 DirectionalExtension._next_probe 同口径）。""" + from ..planner import select_measurement_point + blocked = self.blocked_probes.get(channel, []) + center, radius = region.mec() + if not blocked: + return select_measurement_point(region, self.client.state.position, + self.config).position + safe = safe_disk_radius(region, self.config) + first = blocked[0] + if len(blocked) == 1 and safe >= 0: + dx, dy = center[0] - first[0], center[1] - first[1] + norm = math.hypot(dx, dy) + if norm <= 1e-9: + return (center[0] + safe, center[1]) + scale = min(1.0, safe / norm) + return (center[0] + dx * scale, center[1] + dy * scale) + base = math.atan2(first[1] - center[1], first[0] - center[0]) + azimuth = base + math.radians(45.0 * (len(blocked) - 1)) + return (center[0] + radius * math.cos(azimuth), + center[1] + radius * math.sin(azimuth)) + + # ------------------------------------------------------------ 事件捕获 + def _reset_step_recording(self) -> None: + self._step_events = [] + self._step_clears = [] + + def _record_event(self, **event) -> None: + self._step_events.append(event) diff --git a/src/mcm26b/planner/hrl_loop.py b/src/mcm26b/planner/hrl_loop.py new file mode 100644 index 0000000..3ae12e9 --- /dev/null +++ b/src/mcm26b/planner/hrl_loop.py @@ -0,0 +1,174 @@ +"""P7 分层强化学习闭环（``--strategy hrl`` / ``--q4-strategy hrl``）。 + +边界与保守性声明（与 rl_loop.py 同口径）： +- 高层只在固定候选槽位上做调度决策（证书清除 / 信息增益定位 / 覆盖站 / + Q4 梯子探点与后备清除），候选几何全部由既有规则生成； +- 感知（belief + 网格幸存集）、清除证书、跳测规则、异常留痕完全继承 + :class:`BayesLoop`，不触碰真源保留定理的前提； +- 问题3 的幸存集按全部读数更新（no_signal 删盘）；问题4 定向源的 + no_signal 不能排除朝向遮挡，幸存集只按 direction/near 更新，遮挡 + 证据走 blocked_probes 梯子与后备清除槽位（DirectionalExtension 口径）； +- 高层策略缺失时经 :meth:`heuristic_action` 降级为 BayesLoop 任务排序， + 闭环仍然完备可运行。 + +问题4 发现覆盖使用 49 点网格（七点环的覆盖证明只对全向源成立）。 +""" + +from __future__ import annotations + +from pathlib import Path + +from ..belief import ChannelStatus +from ..config import ProblemConfig, Vec2 +from ..coverage import CoverageLedger, coverage_points +from ..hrl.scheduler import HRLControllerMixin, point_key +from ..protocol.rules import NO_SIGNAL +from .bayes_loop import BayesLoop + + +class HRLLoop(HRLControllerMixin, BayesLoop): + """分层闭环：BayesLoop 感知/证书 + 高层槽位调度（问题3/4 共用）。""" + + def __init__(self, client, config: ProblemConfig = ProblemConfig(), *, + problem: int = 3, high_policy=None, low_policy=None, + trace_executor: bool = False): + super().__init__(client, config) + if problem not in (3, 4): + raise ValueError("problem 必须是 3 或 4") + self.problem = int(problem) + self.high_policy = high_policy + self.low_policy = low_policy + self.trace_executor = bool(trace_executor) + self.executor_trace: list[dict] = [] + self.hrl_decisions: list[dict] = [] + self._step_events: list[dict] = [] + self._step_clears: list[int] = [] + self._cert_cache: dict = {} + self._last_candidates: list = [] + self._obs_mec_cache: dict = {} + self._station_pending_map: dict = {} + # 观测里幸存格数的归一化基准（初始全集），使特征对训练/部署的 + # 网格密度差不变。 + self._initial_alive = {channel: len(pf) + for channel, pf in self.particles.items()} + if problem == 4: + self.directional_observed: list[int] = [] + self.blocked_probes: dict[int, list[Vec2]] = {} + self.fallback_log: list[dict] = [] + # 49 点网格：七点环覆盖证明只对全向源成立 [口径见模块注释] + self.ledger = CoverageLedger(config, points=coverage_points(config)) + + # ------------------------------------------------------------ 主循环 + def _coverage_phase(self) -> None: + """统一任务循环：每步构建候选，由高层策略（或启发式降级）选槽执行。""" + while self.stop_reason is None: + if self._count_limit_reached() or self._virtual_cap_reached(): + return + # 顺路观测会改变信念、证书和候选；必须发生在本轮快照之前。 + if self.high_policy is None and self.problem == 3 \ + and self.config.bayes.opportunistic_measure: + self._measure_at_stop() + if self.stop_reason is not None or self._count_limit_reached() \ + or self._virtual_cap_reached(): + return + candidates = self.build_candidates() + if not candidates: + return + if self.high_policy is not None: + observation = self.hrl_observation(candidates) + mask = self.hrl_action_mask(candidates) + action = self.high_policy.select(observation, mask) + else: + # 降级路径：BayesLoop 任务排序（无 RL 依赖时闭环仍完备） + action = self.heuristic_action(candidates) + if self.candidate_by_slot(candidates, action) is None: + # 非法策略输出不能造成零耗时无限循环；事实记入异常再安全调度。 + self._anomaly("hrl_invalid_policy_action", 0, action=int(action)) + action = self.heuristic_action(candidates) + cand = self.candidate_by_slot(candidates, action) + if cand is not None: + self._log_low_level(cand) + self.hrl_decisions.append({ + "action": int(action), "kind": cand.kind, + "channel": cand.channel, + "virtual_time_s": self.client.state.virtual_time_s}) + self.hrl_step(action, candidates=candidates) + + # ------------------------------------------------------------ 感知记录 + def _record_measure(self, channel: int, position: Vec2, outcome) -> None: + """BayesLoop._record_measure 的事件捕获版；问题4 no_signal 不删格。 + + Q4 语义：no_signal 可能是朝向遮挡 [观测推理]，删除幸存格会破坏 + 真源保留，因此问题4 幸存集只按 direction/near 更新；缺席证明 + 交由 49 点网格覆盖账本（继承的 _mark_absent_after_coverage）。 + """ + key = point_key(position) + redundant = key in self._measured_points.get(channel, set()) + belief = self.belief.channels[channel] + was_unknown = belief.status is ChannelStatus.UNKNOWN + pf = self.particles[channel] + before = len(pf) + self.belief.update_measure(channel, position, outcome.result, + outcome.svd_deg) + self._measured_points.setdefault(channel, set()).add(key) + self._clear_failed.discard(channel) + belief = self.belief.channels[channel] + if belief.status not in (ChannelStatus.CLEARED, ChannelStatus.ABSENT): + if not (self.problem == 4 and outcome.result == NO_SIGNAL): + pf.apply_measure(position, outcome.result, outcome.svd_deg) + if not len(pf): + if belief.status is ChannelStatus.UNKNOWN: + self.belief.mark_absent(channel) + if channel not in self.pf_absent_channels: + self.pf_absent_channels.append(channel) + else: + self._anomaly("pf_empty_while_detected", channel, + position=position) + self._record_event(channel=channel, result=outcome.result, + pf_before=before, pf_after=len(pf), + redundant=redundant, was_unknown=was_unknown) + + def _attempt_clear(self, channel: int, target: Vec2, cert) -> bool: + success = super()._attempt_clear(channel, target, cert) + if success and hasattr(self, "blocked_probes"): + self.blocked_probes.pop(channel, None) + if success: + self._step_clears.append(channel) + return success + + # ------------------------------------------------------------ 低层执行留痕 + def _log_low_level(self, cand) -> None: + """低层到达推演留痕（本地步进，不与模拟器交互，不产生虚拟耗时）。""" + if not self.trace_executor: + return + from ..hrl.executor import LowLevelExecutor + executor = getattr(self, "_executor", None) + if executor is None: + executor = self._executor = LowLevelExecutor(self.low_policy) + waypoints = executor.roll(self.client.state.position, cand.position) + if self.low_policy is not None: + self.executor_trace.append({ + "kind": cand.kind, "channel": cand.channel, + "goal": [round(v, 2) for v in cand.position], + "waypoints": [[round(v, 2) for v in p] for p in waypoints], + "navigation": dict(executor.last_roll_info), + }) + + # ------------------------------------------------------------ 收尾 + def _summary(self, info) -> dict: + report = super()._summary(info) + report["strategy"] = "hrl_q4" if self.problem == 4 else "hrl" + report["hrl_problem"] = self.problem + kinds: dict[str, int] = {} + for decision in self.hrl_decisions: + kinds[decision["kind"]] = kinds.get(decision["kind"], 0) + 1 + report["hrl_action_kinds"] = kinds + report["hrl_decisions"] = self.hrl_decisions + report["hrl_executor_trace"] = self.executor_trace + if self.problem == 4: + report["directional_observed_channels"] = \ + list(self.directional_observed) + report["fallback_clears"] = self.fallback_log + report["fallback_clear_attempts"] = sum( + entry.get("attempts", 0) for entry in self.fallback_log) + return report diff --git a/src/mcm26b/training_ui/__init__.py b/src/mcm26b/training_ui/__init__.py new file mode 100644 index 0000000..ea762b2 --- /dev/null +++ b/src/mcm26b/training_ui/__init__.py @@ -0,0 +1,15 @@ +"""模型训练前端：本地回环 Web UI + 训练子进程监控。 + +启动： + python -m mcm26b.training_ui --ui-port 8036 +或： + python scripts/train_hrl_ui.py --ui-port 8036 + +子命令： + python -m mcm26b.training_ui --print-schema # 配置契约 + python -m mcm26b.training_ui --print-preview # 离线示例快照 +""" +from .server import TrainingUI, TrainingJob, run_training_ui +from .schema import config_schema + +__all__ = ["TrainingUI", "TrainingJob", "run_training_ui", "config_schema"] diff --git a/src/mcm26b/training_ui/__main__.py b/src/mcm26b/training_ui/__main__.py new file mode 100644 index 0000000..2a16926 --- /dev/null +++ b/src/mcm26b/training_ui/__main__.py @@ -0,0 +1,4 @@ +from .server import run_training_ui + +if __name__ == "__main__": + raise SystemExit(run_training_ui()) diff --git a/src/mcm26b/training_ui/demo.py b/src/mcm26b/training_ui/demo.py new file mode 100644 index 0000000..161c326 --- /dev/null +++ b/src/mcm26b/training_ui/demo.py @@ -0,0 +1,211 @@ +"""训练前端示例快照：把一段"看起来像真的" PPO 训练曲线合成为合法快照。 + +用途有两个，都不涉及真实训练： +1. ``GET /api/train/state?preview=1`` 让界面在没有训练进程时也能验证渲染 + （曲线、指标卡、进程时间线、日志着色、逐回合表）； +2. 单元测试可以断言前端的收敛判读分支（上升/平坦/KL 过猛）各有稳定输入。 + +曲线是解析函数 + 固定种子的确定性噪声，不含任何真实评测数据。 +""" + +from __future__ import annotations + +import math +import random +import time +from typing import Any + +__all__ = ["DEMO_CONFIG", "DEMO_SCENARIOS", "demo_metrics", "demo_monitor", + "demo_log_lines", "demo_snapshot"] + +DEMO_SCENARIOS = ("rising", "plateau", "kl-spike") + +DEMO_CONFIG: dict[str, Any] = { + "problem": 3, "stage": 2, "timesteps": 12000, "seed": 320001, + "envs": 4, "vec_env": "subproc", "device": "mps", + "config_profile": "production", "reward_profile": "time", + "model": "output/hrl/ui/demo/q3.zip", "init": None, "resume": False, + "eval_episodes": 2, "rollout_steps": 256, "batch_size": 256, + "n_epochs": 10, "max_wall_s": 1200.0, "local_selector": "geometry", + "local_model": None, "verbose": 1, +} + +DEMO_COMMAND = [ + "python", "scripts/train_hrl_high.py", "--problem", "3", "--stage", "2", + "--timesteps", "12000", "--seed", "320001", "--envs", "4", + "--vec-env", "subproc", "--device", "mps", "--config-profile", "production", + "--reward-profile", "time", "--model", "output/hrl/ui/demo/q3.zip", + "--eval-episodes", "2", "--rollout-steps", "256", "--batch-size", "256", + "--n-epochs", "10", "--max-wall-s", "1200.0", "--local-selector", + "geometry", "--progress-json", "--verbose", "1", +] + +DEMO_STEPS = 48 + + +def _finite(value: float) -> float | None: + if value != value or value in (float("inf"), float("-inf")): + return None + return round(value, 8) + + +def demo_metrics(count: int = DEMO_STEPS, steps: int = 12000, *, + seed: int = 7, scenario: str = "rising", + total_steps: int | None = None, fps: float = 420.0 + ) -> list[dict[str, Any]]: + """合成 count 个进度点，横轴覆盖到 ``total_steps`` 的 ``count/DEMO_STEPS``。 + + 场景（用于验证收敛判读的各个分支，不含真实数据）： + rising 奖励对数饱和上升、KL 正常 → "收敛趋势良好" + plateau 奖励已进入平台期 → "已趋于平稳（疑似收敛）" + kl-spike 末段近似 KL 冲高 → "KL 偏大，策略更新可能过猛" + """ + if scenario not in DEMO_SCENARIOS: + raise ValueError(f"未知示例场景: {scenario}") + total = int(total_steps if total_steps is not None else steps) + rng = random.Random(seed) + points: list[dict[str, Any]] = [] + per = total / DEMO_STEPS + ceiling = 42.0 + span = min(1.0, max(1e-6, count / float(DEMO_STEPS))) + for index in range(1, count + 1): + t = (index / float(DEMO_STEPS)) / span # 折到整段训练的进度比例 + steps_now = int(round(index * per)) + if scenario == "plateau": + reward = 41.5 + rng.uniform(-0.35, 0.35) + value_loss_curve = 3.6 + rng.uniform(-0.2, 0.2) + approx_kl = max(0.001, 0.004 + rng.uniform(-0.001, 0.001)) + explained = min(0.95, 0.88 + rng.uniform(-0.02, 0.02)) + elif scenario == "kl-spike": + reward = ceiling * (1.0 - math.exp(-3.1 * t)) + rng.uniform(-1.2, 1.2) + value_loss_curve = 62.0 * math.exp(-2.4 * t) + 3.4 + base_kl = 0.031 * math.exp(-1.7 * t) + approx_kl = base_kl + (0.075 if t > 0.8 else 0.0) + rng.uniform(-0.003, 0.003) + explained = -0.09 + 1.05 * (1.0 - math.exp(-2.2 * t)) + else: + noise = rng.uniform(-1.4, 1.4) * (1.0 - 0.6 * t) + reward = ceiling * (1.0 - math.exp(-3.1 * t)) + noise + value_loss_curve = 62.0 * math.exp(-2.4 * t) + 3.4 + approx_kl = 0.031 * math.exp(-1.7 * t) + explained = -0.09 + 1.05 * (1.0 - math.exp(-2.2 * t)) + value_loss = value_loss_curve + rng.uniform(-0.5, 0.5) + policy_loss = -0.021 + 0.006 * math.sin(index / 4.0) + rng.uniform(-0.002, 0.002) + entropy = 1.35 - 0.55 * (t if scenario != "plateau" else 0.9) \ + + rng.uniform(-0.02, 0.02) + points.append({ + "timesteps": steps_now, + "n_updates": index * 12, + "ep_rew_mean": _finite(reward), + "ep_len_mean": _finite(24.0 + 21.0 * t + rng.uniform(-1.5, 1.5)), + "value_loss": _finite(value_loss), + "policy_gradient_loss": _finite(policy_loss), + "approx_kl": _finite(max(0.0008, approx_kl + rng.uniform(-0.002, 0.002))), + "entropy_loss": _finite(entropy), + "explained_variance": _finite(min(0.97, explained + + rng.uniform(-0.03, 0.03))), + # 吞吐尽量接近真实量级（envs=4 时约 400+ 步/秒），否则剩余时间 + # 估计会给出荒谬的 ETA + "fps": _finite(max(30.0, fps * (0.92 + 0.16 * t) + + rng.uniform(-12.0, 12.0))), + }) + return points + + +def demo_monitor(count: int = 60, seed: int = 11) -> list[dict[str, Any]]: + rng = random.Random(seed) + rows = [] + clock = 0.0 + for index in range(count): + t = index / max(1, count - 1) + clock += rng.uniform(4.0, 14.0) + rows.append({ + "r": _finite(38.0 * (1.0 - math.exp(-3.0 * t)) + rng.uniform(-3.0, 3.0)), + "l": float(rng.randint(28, 74)), + "t": round(clock, 4), + }) + return rows + + +def demo_log_lines() -> list[str]: + """一段包含各类着色分支的日志：SB3 表格、进度 JSON、警告与收尾。""" + lines = [ + "=== 训练前端离线预览：以下为示例快照，非真实训练输出 ===", + "使用设备: mps", + "创建 4 个 DummyVecEnv/SubprocVecEnv 环境 (stage=2, problem=3)", + "Monitor CSV: output/hrl/ui/demo/high_curve_q3_env0.monitor.csv", + "-----------------------------", + "| rollout/ | |", + "| ep_len_mean | 31.4 |", + "| ep_rew_mean | 12.83 |", + "| time/ | |", + "| fps | 412 |", + "| total_timesteps | 512 |", + "-----------------------------", + ] + for index in range(1, 9): + t = index / 8.0 + lines.append( + '{"event": "progress", "timesteps": %d, "n_updates": %d, ' + '"ep_rew_mean": %.3f, "ep_len_mean": %.2f, "value_loss": %.4f, ' + '"policy_gradient_loss": %.5f, "approx_kl": %.5f, ' + '"entropy_loss": %.4f, "explained_variance": %.4f, "fps": %.1f}' + % (index * 1500, index * 12, 42.0 * (1 - math.exp(-3.1 * t)), + 24 + 21 * t, 62.0 * math.exp(-2.4 * t) + 3.4, -0.021 + 0.006 * t, + 0.031 * math.exp(-1.7 * t), 1.35 - 0.55 * t, + -0.09 + 1.05 * (1 - math.exp(-2.2 * t)), 390 + 70 * t) + ) + lines += [ + "UserWarning: 示例警告行，用于验证警告着色", + "确定性评估：2/2 成功，accounted=20，无异常", + "模型已保存: output/hrl/ui/demo/q3.zip", + "元数据已写入: output/hrl/ui/demo/q3.json", + ] + return lines + + +def demo_snapshot(*, log_tail: int = 400, scenario: str = "rising", + done_ratio: float = 0.62, fps: float = 420.0) -> dict[str, Any]: + """合成一次"跑到中途"的快照（键与 TrainingJob.snapshot 一致）。 + + ``done_ratio`` 控制已完成的步数比例，用来让预览更接近真实运行中状态 + （而不是刚好 100%）；``scenario`` 选择收敛判读分支；``fps`` 决定剩余 + 时间估计的吞吐量级。 + """ + from .server import describe_process, summarize_convergence + + now = time.time() + ratio = min(1.0, max(0.05, float(done_ratio))) + metrics = demo_metrics(count=max(6, int(round(DEMO_STEPS * ratio))), + total_steps=int(DEMO_CONFIG["timesteps"]), + scenario=scenario, fps=fps) + monitor = demo_monitor(count=max(6, int(round(60 * ratio)))) + config = dict(DEMO_CONFIG) + lines = demo_log_lines() + logs = (lines * 12)[-max(1, int(log_tail)):] + status = "running" + payload: dict[str, Any] = { + "job_id": "job-preview", + "status": status, + "pid": 42424, + "created_at": now - 186.0, + "started_at": now - 184.0, + "finished_at": None, + "elapsed_s": 184.0, + "returncode": None, + "error": None, + "command": list(DEMO_COMMAND), + "model": str(config["model"]), + "config": config, + "log_lines": logs, + "log_total": len(logs) * 3, + "metrics": metrics, + "metric_offset": 0, + "metric_end": len(metrics), + "metric_total": len(metrics), + "monitor": monitor, + "preview": True, + "preview_scenario": scenario, + } + payload["convergence"] = summarize_convergence(metrics, monitor) + payload["process"] = describe_process(payload) + return payload diff --git a/src/mcm26b/training_ui/schema.py b/src/mcm26b/training_ui/schema.py new file mode 100644 index 0000000..04d9da6 --- /dev/null +++ b/src/mcm26b/training_ui/schema.py @@ -0,0 +1,308 @@ +"""训练前端配置契约：字段元数据、分组、预设与指标字典。 + +为什么单独成模块：前端表单的取值域、单位、分组与后端 ``TrainConfig`` 的 +校验规则必须来自同一处，否则界面会允许后端拒绝的输入（或反之）。这里只 +声明"可展示"的元数据，真正的合法性判定仍由 :class:`TrainConfig.validate` +单点负责——前端只做提前提示，不复制规则。 + +指标字典 ``METRIC_CATALOG`` 同样是唯一出处：``server.summarize_convergence`` +用它取数，``/api/train/schema`` 把它交给前端画图，避免两边各写一份键名。 +""" + +from __future__ import annotations + +from typing import Any + +__all__ = [ + "CONFIG_FIELDS", "CONFIG_GROUPS", "PRESETS", "METRIC_CATALOG", + "config_schema", "preset_payloads", "metric_keys", +] + +# 字段元数据： +# kind number|integer|text|select|bool +# 其余键 前端直接使用（label/unit/group/choices/min/max/step/help/advanced） +CONFIG_FIELDS: list[dict[str, Any]] = [ + # ---------------------------------------------------------- 环境与课程 + { + "key": "problem", "kind": "select", "group": "env", + "label": "问题", "default": 3, "choices": [ + {"value": 3, "label": "问题 3（全向源）"}, + {"value": 4, "label": "问题 4（含定向源）"}, + ], + "help": "决定场景源类型与末段课程配置。", + }, + { + "key": "stage", "kind": "select", "group": "env", + "label": "课程阶段", "default": 1, "choices": [ + {"value": 1, "label": "1 · 单源 / 5 频道 / 无噪"}, + {"value": 2, "label": "2 · 3 源 / 10 频道 / ±0.5°"}, + {"value": 3, "label": "3 · 8 源 / 16 频道 / ±1°"}, + {"value": 4, "label": "4 · 10–16 源 / 20 频道 / ±1°"}, + ], + "help": "课程递进；跨阶段续训用下方 --init 迁移权重。", + }, + { + "key": "config_profile", "kind": "select", "group": "env", + "label": "配置档", "default": "production", "choices": [ + {"value": "production", "label": "production（生产保真）"}, + {"value": "coarse-pretrain", "label": "coarse-pretrain（快速粗训）"}, + ], + "help": "coarse-pretrain 显式映射为 fast 评分配置并写入模型元数据。", + }, + { + "key": "reward_profile", "kind": "select", "group": "env", + "label": "奖励口径", "default": "time", "choices": [ + {"value": "time", "label": "time（P0.4 纯时间目标）"}, + {"value": "prompt", "label": "prompt（§5.2 原始塑形）"}, + ], + "help": "对照实验用；口径固化进模型元数据，不静默替换。", + }, + { + "key": "seed", "kind": "integer", "group": "env", + "label": "随机种子", "default": 320001, "min": 0, "max": 2_147_483_647, + "help": "不同种子用于多次重复训练与方差估计。", + }, + # ---------------------------------------------------------- PPO 超参 + { + "key": "rollout_steps", "kind": "integer", "group": "ppo", + "label": "采样步数 n_steps", "default": 128, "min": 1, "max": 100_000, + "help": "每次更新前每个环境采集的步数；近似一个 rollout 的宽度。", + }, + { + "key": "batch_size", "kind": "integer", "group": "ppo", + "label": "批大小 batch_size", "default": 128, "min": 1, "max": 100_000, + "help": "建议 batch_size ≤ n_steps × envs。", + }, + { + "key": "n_epochs", "kind": "integer", "group": "ppo", + "label": "更新轮数 n_epochs", "default": 3, "min": 1, "max": 100, + "help": "每批数据的 SGD 轮数；过大易使 KL 飙升。", + }, + # ---------------------------------------------------------- 运行环境 + { + "key": "envs", "kind": "integer", "group": "runtime", + "label": "并行环境数", "default": 1, "min": 1, "max": 32, + "help": "SubprocVecEnv 下每个 worker 一个进程与 Monitor 文件。", + }, + { + "key": "vec_env", "kind": "select", "group": "runtime", + "label": "向量化方式", "default": "dummy", "choices": [ + {"value": "dummy", "label": "dummy（单进程，调试友好）"}, + {"value": "subproc", "label": "subproc（多进程，吞吐优先）"}, + ], + "help": "envs>1 且追求吞吐时选 subproc。", + }, + { + "key": "device", "kind": "select", "group": "runtime", + "label": "计算设备", "default": "auto", "choices": [ + {"value": "auto", "label": "auto（自动选择）"}, + {"value": "cpu", "label": "cpu"}, + {"value": "cuda", "label": "cuda（NVIDIA）"}, + {"value": "mps", "label": "mps（Apple 芯片）"}, + ], + "help": "CPU 路径最稳；GPU 需与 torch 构建匹配。", + }, + { + "key": "local_selector", "kind": "select", "group": "runtime", + "label": "低层执行器", "default": "geometry", "choices": [ + {"value": "geometry", "label": "geometry（几何选点，默认）"}, + {"value": "rl", "label": "rl（低层 PPO，接口预留）"}, + ], + "help": "rl 仅记录元数据，当前 HTTP 执行路径仍由高层宏动作终点决定。", + }, + # ---------------------------------------------------------- 训练规模 + { + "key": "timesteps", "kind": "integer", "group": "scale", + "label": "总步数 timesteps", "default": 2048, "min": 1, + "max": 10_000_000, "help": "本次训练的环境步总数（续训时为增量步数）。", + }, + { + "key": "max_wall_s", "kind": "number", "group": "scale", + "label": "墙钟上限 (s)", "default": 1200.0, "min": 0.1, "max": 7200.0, + "step": 30, "help": "硬上限：到点由回调主动停止并保存模型。", + }, + { + "key": "eval_episodes", "kind": "integer", "group": "scale", + "label": "评估回合数", "default": 2, "min": 0, "max": 1000, + "help": "训练结束后确定性评估；0 表示跳过。", + }, + { + "key": "verbose", "kind": "select", "group": "scale", + "label": "SB3 日志级别", "default": 1, "choices": [ + {"value": 0, "label": "0 · 静默（只留 JSON 进度）"}, + {"value": 1, "label": "1 · 表格（默认）"}, + {"value": 2, "label": "2 · 调试"}, + ], + "help": "前端已解析 SB3 表格，默认 1 便于对照原始输出。", + }, + # ---------------------------------------------------------- 模型路径 + { + "key": "model", "kind": "text", "group": "paths", + "label": "模型输出路径", "default": "output/hrl/ui/q3.zip", + "help": "相对仓库根；同目录会写入 .json 元数据与 Monitor CSV。", + }, + { + "key": "init", "kind": "text", "group": "paths", "optional": True, + "label": "初始权重 --init", "default": None, + "help": "留空=从零；填上一阶段 .zip 即课程迁移（步数计数重置）。", + }, + { + "key": "local_model", "kind": "text", "group": "paths", "optional": True, + "label": "低层模型 --local-model", "default": None, + "advanced": True, + "help": "仅 local_selector=rl 时有意义。", + }, + { + "key": "resume", "kind": "bool", "group": "paths", + "label": "续训 --resume", "default": False, + "help": "从模型输出路径已有 .zip 继续，timesteps 按增量理解。", + }, + { + "key": "force", "kind": "bool", "group": "paths", + "label": "覆盖已有模型 --force", "default": False, + "help": "输出路径已存在时必须勾选，否则训练脚本会拒绝覆盖。与续训互斥。", + }, +] + +CONFIG_GROUPS: list[dict[str, str]] = [ + {"key": "env", "label": "环境与课程", "hint": "题目、课程阶段与奖励口径"}, + {"key": "ppo", "label": "PPO 超参数", "hint": "采样宽度与更新强度"}, + {"key": "runtime", "label": "运行环境", "hint": "并行、设备与低层执行器"}, + {"key": "scale", "label": "训练规模", "hint": "步数、墙钟上限与评估"}, + {"key": "paths", "label": "模型与迁移", "hint": "产物路径、续训与课程迁移"}, +] + +# 预设 = 只覆盖差异字段；后端 from_payload 会补齐其余默认值。 +PRESETS: list[dict[str, Any]] = [ + { + "key": "smoke", + "label": "冒烟（约 1 分钟）", + "note": "64 步、CPU、无评估——只验证链路与界面。", + "payload": { + "problem": 3, "stage": 1, "timesteps": 64, "seed": 1, + "envs": 1, "vec_env": "dummy", "device": "cpu", + "config_profile": "coarse-pretrain", "reward_profile": "time", + "model": "output/hrl/ui/smoke/q3.zip", "eval_episodes": 0, + "rollout_steps": 32, "batch_size": 32, "n_epochs": 2, + "max_wall_s": 180.0, "verbose": 1, "force": True, + }, + }, + { + "key": "local-check", + "label": "本地验证（约 10 分钟）", + "note": "stage 1 单源课程 2048 步 + 2 回合评估。", + "payload": { + "problem": 3, "stage": 1, "timesteps": 2048, "seed": 320001, + "envs": 1, "vec_env": "dummy", "device": "auto", + "config_profile": "production", "reward_profile": "time", + "model": "output/hrl/ui/local/q3.zip", "eval_episodes": 2, + "rollout_steps": 128, "batch_size": 128, "n_epochs": 3, + "max_wall_s": 900.0, "verbose": 1, "force": True, + }, + }, + { + "key": "q3-main", + "label": "Q3 主训练", + "note": "stage 3 八源课程，4 进程并行；建议在训练机上跑。", + "payload": { + "problem": 3, "stage": 3, "timesteps": 200000, "seed": 320001, + "envs": 4, "vec_env": "subproc", "device": "auto", + "config_profile": "production", "reward_profile": "time", + "model": "output/hrl/ui/q3-main/q3.zip", "eval_episodes": 5, + "rollout_steps": 256, "batch_size": 256, "n_epochs": 10, + "max_wall_s": 7200.0, "verbose": 1, + }, + }, + { + "key": "q4-transfer", + "label": "Q4 课程迁移", + "note": "从 Q3 权重迁移到问题 4 末段课程（含定向源）。", + "payload": { + "problem": 4, "stage": 4, "timesteps": 120000, "seed": 420001, + "envs": 4, "vec_env": "subproc", "device": "auto", + "config_profile": "production", "reward_profile": "time", + "model": "output/hrl/ui/q4-main/q4.zip", + "init": "output/hrl/ui/q3-main/q3.zip", "eval_episodes": 5, + "rollout_steps": 256, "batch_size": 256, "n_epochs": 10, + "max_wall_s": 7200.0, "verbose": 1, + }, + }, +] + +# 指标字典：键来自训练脚本 --progress-json 的单行 JSON（短名）与 SB3 表格 +# （归一化去前缀）。前端按此顺序绘图，收敛判据在 server.summarize_convergence。 +METRIC_CATALOG: list[dict[str, Any]] = [ + { + "key": "ep_rew_mean", "label": "平均回合奖励", "role": "primary", + "color": "#4f8cff", "format": "number", "better": "up", + "help": "最近若干回合的平均回报；主要收敛判据。", + }, + { + "key": "ep_len_mean", "label": "平均回合长度", "role": "secondary", + "color": "#38bdf8", "format": "number", "better": "neutral", + "help": "回合步数；与奖励同时上升通常代表策略更完整而非更快。", + }, + { + "key": "approx_kl", "label": "近似 KL", "role": "stability", + "color": "#f59e0b", "format": "sci", "better": "down", + "help": "策略更新幅度；> 0.05 视为更新过猛。", + }, + { + "key": "explained_variance", "label": "解释方差", "role": "stability", + "color": "#34d399", "format": "number", "better": "up", + "help": "价值函数拟合优度；< 0 表示还不如均值预测。", + }, + { + "key": "value_loss", "label": "价值损失", "role": "stability", + "color": "#f87171", "format": "sci", "better": "down", + "help": "critic 拟合误差。", + }, + { + "key": "policy_gradient_loss", "label": "策略梯度损失", "role": "stability", + "color": "#c084fc", "format": "sci", "better": "neutral", + "help": "actor 的代理损失；量级小且符号可变属正常。", + }, + { + "key": "entropy_loss", "label": "策略熵", "role": "exploration", + "color": "#fbbf24", "format": "number", "better": "neutral", + "help": "探索度；若长期贴近 0 说明策略过早确定。", + }, + { + "key": "n_updates", "label": "更新次数", "role": "throughput", + "color": "#93a1bd", "format": "integer", "better": "up", + "help": "已完成的 PPO 更新轮数。", + }, + { + "key": "fps", "label": "吞吐 FPS", "role": "throughput", + "color": "#22d3ee", "format": "number", "better": "up", + "help": "每秒环境步数，反映硬件与并行配置是否吃满。", + }, +] + +_METRIC_BY_KEY = {item["key"]: item for item in METRIC_CATALOG} + + +def metric_keys() -> list[str]: + return [item["key"] for item in METRIC_CATALOG] + + +def metric_meta(key: str) -> dict[str, Any]: + return _METRIC_BY_KEY.get(key, {"key": key, "label": key, "role": "other", + "color": "#93a1bd", "format": "number", + "better": "neutral", "help": ""}) + + +def config_schema() -> dict[str, Any]: + """返回给前端的完整配置契约（字段 + 分组 + 预设 + 指标字典）。""" + return { + "fields": CONFIG_FIELDS, + "groups": CONFIG_GROUPS, + "presets": [{"key": item["key"], "label": item["label"], + "note": item["note"], "payload": dict(item["payload"])} + for item in PRESETS], + "metrics": METRIC_CATALOG, + } + + +def preset_payloads() -> dict[str, dict[str, Any]]: + return {item["key"]: dict(item["payload"]) for item in PRESETS} diff --git a/src/mcm26b/training_ui/server.py b/src/mcm26b/training_ui/server.py new file mode 100644 index 0000000..f899d54 --- /dev/null +++ b/src/mcm26b/training_ui/server.py @@ -0,0 +1,1082 @@ +"""训练前端服务器：在评测前端同一套「本地回环 HTTP + 静态白名单」模式上， +额外托管一个受控的训练子进程，并把 stdout/JSON 进度流做成轮询 API。 + +设计边界： +- 只监听 127.0.0.1，只允许白名单静态文件； +- 只允许一个训练任务，命令由服务端用参数列表构造，不经过 shell； +- 支持启动/停止/状态查询；停止时发送 SIGTERM 到独立进程组； +- 训练进程通过 ``--progress-json`` 输出单行 JSON 进度，服务端同时兼容 + Stable-Baselines3 的表格输出和 Monitor CSV。 + +界面契约（供 ``static/`` 使用）： +- ``GET /api/train/schema`` 字段/分组/预设/指标字典（``schema.py`` 单一出处） +- ``GET /api/train/state`` 快照；``after_metric=N`` 只回增量指标 +- ``POST /api/train/start`` 以部分字段启动（其余取默认值） +- ``POST /api/train/stop`` SIGTERM 独立进程组 +""" + +from __future__ import annotations + +import json +import os +import re +import signal +import subprocess +import sys +import threading +import time +from collections import deque +from dataclasses import dataclass +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from pathlib import Path +from typing import Any +from urllib.parse import parse_qs, urlsplit + +from .demo import DEMO_SCENARIOS, demo_snapshot +from .schema import config_schema + +__all__ = ["TrainingJob", "TrainingUI", "run_training_ui", "build_command", + "summarize_convergence", "describe_process"] + +STATIC_DIR = Path(__file__).resolve().parent / "static" +REPO_ROOT = Path(__file__).resolve().parents[3] +TRAIN_SCRIPT = REPO_ROOT / "scripts" / "train_hrl_high.py" +STATIC_FILES = { + "/": ("index.html", "text/html; charset=utf-8"), + "/index.html": ("index.html", "text/html; charset=utf-8"), + "/app.js": ("app.js", "application/javascript; charset=utf-8"), + "/charts.js": ("charts.js", "application/javascript; charset=utf-8"), + "/style.css": ("style.css", "text/css; charset=utf-8"), +} + +MAX_LOG_LINES = 4000 +MAX_METRICS = 5000 + +_NUM_RE = re.compile(r"^[+-]?(?:\d+\.?\d*|\.\d+)(?:[eE][+-]?\d+)?$") +_SB3_SECTIONS = ("rollout/", "time/", "train/", "system/") +# 路径/字符串参数只拒绝控制字符（含 \r\n\t 与 DEL），不做 shell 元字符过滤： +# 命令走参数列表、不经过 shell，这里只是防日志与命令行被注入控制序列。 +# 注意必须用单反斜杠转义——写成 r"...\\x00" 会变成"字面反斜杠到反斜杠"的 +# 字符类，从而把所有含 `\` 或看起来像路径分隔的输入误判为非法。 +_SAFE_ARG_RE = re.compile(r"^[^\x00-\x1f\x7f]{1,512}$") + + +def _finite_float(value: Any) -> float | None: + if value is None or isinstance(value, bool): + return None + if isinstance(value, (int, float)): + value = float(value) + if value != value or value in (float("inf"), float("-inf")): + return None + return value + text = str(value).strip() + if not _NUM_RE.match(text): + return None + try: + return float(text) + except ValueError: + return None + + +def _normalize_metric(key: str) -> str: + """把 SB3 的 ``rollout/ep_rew_mean`` 与进度 JSON 的 ``ep_rew_mean`` + 归一化到同一短名，便于前端绘制。""" + key = key.strip().replace(" ", "_") + if "/" in key: + key = key.rsplit("/", 1)[-1] + return key + + +@dataclass(frozen=True) +class TrainConfig: + problem: int = 3 + stage: int = 1 + timesteps: int = 2048 + seed: int = 320001 + envs: int = 1 + vec_env: str = "dummy" + device: str = "auto" + config_profile: str = "production" + reward_profile: str = "time" + model: str = "output/hrl/ui/q3.zip" + init: str | None = None + resume: bool = False + force: bool = False + eval_episodes: int = 2 + rollout_steps: int = 128 + batch_size: int = 128 + n_epochs: int = 3 + max_wall_s: float = 1200.0 + local_selector: str = "geometry" + local_model: str | None = None + verbose: int = 1 + + @classmethod + def from_payload(cls, payload: dict[str, Any]) -> "TrainConfig": + if not isinstance(payload, dict): + raise ValueError("请求体必须是 JSON 对象") + defaults = cls() + data: dict[str, Any] = {} + for field in cls.__dataclass_fields__: + if field in payload and payload[field] is not None: + data[field] = payload[field] + cfg = cls(**{**defaults.__dict__, **data}) + cfg.validate() + return cfg + + def validate(self) -> None: + if self.problem not in (3, 4): + raise ValueError("problem 必须是 3 或 4") + if self.stage not in (1, 2, 3, 4): + raise ValueError("stage 必须在 1..4") + if not 1 <= int(self.timesteps) <= 10_000_000: + raise ValueError("timesteps 必须在 1..10,000,000") + if bool(self.resume) and bool(self.force): + raise ValueError("--resume 与 --force 互斥：续训不可能同时覆盖") + if not 1 <= int(self.envs) <= 32: + raise ValueError("envs 必须在 1..32") + if self.vec_env not in ("dummy", "subproc"): + raise ValueError("vec_env 必须是 dummy 或 subproc") + if self.device not in ("auto", "cpu", "cuda", "mps"): + raise ValueError("device 必须是 auto/cpu/cuda/mps") + if self.config_profile not in ("production", "coarse-pretrain"): + raise ValueError("config_profile 非法") + if self.reward_profile not in ("time", "prompt"): + raise ValueError("reward_profile 非法") + if self.local_selector not in ("geometry", "rl"): + raise ValueError("local_selector 非法") + if not 1 <= int(self.rollout_steps) <= int(self.timesteps): + raise ValueError("rollout_steps 必须在 1..timesteps") + if not 1 <= int(self.n_epochs) <= 100: + raise ValueError("n_epochs 必须在 1..100") + if not 0.0 < float(self.max_wall_s) <= 7200.0: + raise ValueError("max_wall_s 必须在 0..7200 秒") + if int(self.eval_episodes) < 0: + raise ValueError("eval_episodes 不能为负") + for label, value in (("model", self.model), ("init", self.init), + ("local_model", self.local_model)): + if value is None: + continue + if not isinstance(value, str) or not _SAFE_ARG_RE.match(value): + raise ValueError(f"{label} 路径含非法字符") + + @property + def model_path(self) -> Path: + return (REPO_ROOT / self.model).resolve() if not Path(self.model).is_absolute() \ + else Path(self.model).resolve() + + def command(self) -> list[str]: + return build_command(self) + + +def _int_query(params: dict[str, list[str]], name: str, default: int | None, + low: int, high: int, *, optional: bool = False) -> int | None: + """解析整数查询参数；越界或非整数一律报错，不用静默钳制掩盖调用错误。""" + if name not in params: + return None if optional else default + raw = (params[name] or [""])[0].strip() + if raw == "": + return None if optional else default + try: + value = int(raw) + except ValueError: + raise ValueError(f"{name} 必须是整数") from None + if not low <= value <= high: + raise ValueError(f"{name} 必须在 {low}..{high}") + return value + + +def build_command(config: TrainConfig) -> list[str]: + if not TRAIN_SCRIPT.is_file(): + raise FileNotFoundError(f"训练脚本不存在: {TRAIN_SCRIPT}") + cmd = [ + sys.executable, str(TRAIN_SCRIPT), + "--problem", str(config.problem), + "--stage", str(config.stage), + "--timesteps", str(config.timesteps), + "--seed", str(config.seed), + "--envs", str(config.envs), + "--vec-env", config.vec_env, + "--device", config.device, + "--config-profile", config.config_profile, + "--reward-profile", config.reward_profile, + "--model", str(config.model_path), + "--eval-episodes", str(config.eval_episodes), + "--rollout-steps", str(config.rollout_steps), + "--batch-size", str(config.batch_size), + "--n-epochs", str(config.n_epochs), + "--max-wall-s", str(config.max_wall_s), + "--local-selector", config.local_selector, + "--progress-json", + "--verbose", str(config.verbose), + ] + if config.resume: + cmd.append("--resume") + if config.force: + cmd.append("--force") + if config.init: + cmd.extend(["--init", str((REPO_ROOT / config.init).resolve() + if not Path(config.init).is_absolute() + else config.init)]) + if config.local_model: + cmd.extend(["--local-model", str(config.local_model)]) + return cmd + + +class TrainingJob: + """单个训练子进程的生命周期、日志和指标缓存。""" + + def __init__(self, config: TrainConfig): + self.config = config + self.job_id = f"job-{int(time.time() * 1000)}" + self.command = config.command() + self.created_at = time.time() + self.started_at: float | None = None + self.finished_at: float | None = None + self.returncode: int | None = None + self.status = "created" # created/running/exited/failed/stopped + self.error: str | None = None + self.process: subprocess.Popen | None = None + self.logs: deque[str] = deque(maxlen=MAX_LOG_LINES) + self.metrics: deque[dict[str, Any]] = deque(maxlen=MAX_METRICS) + self.monitor: list[dict[str, Any]] = [] + self._lock = threading.RLock() + self._reader: threading.Thread | None = None + self._table_sections: list[tuple[str, dict[str, Any]]] = [] + self._table_steps: float | None = None # 当前表的 total_timesteps + self._pending_table: dict[str, Any] | None = None # 未见刻度的半张表 + self._last_table_steps: float | None = None # 上一张表已知刻度（截断兜底） + self._last_json_steps: float | None = None # 最近一条 progress JSON 的步数 + self._last_monitor_sig: tuple[int, int] | None = None + + # ------------------------------------------------------------ lifecycle + def start(self) -> None: + with self._lock: + if self.process is not None and self.process.poll() is None: + raise RuntimeError("已有训练任务在运行") + env = os.environ.copy() + env["PYTHONUNBUFFERED"] = "1" + env["HRL_UI_JOB_ID"] = self.job_id + self.process = subprocess.Popen( + self.command, + cwd=str(REPO_ROOT), + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, + env=env, + start_new_session=True, + ) + self.started_at = time.time() + self.status = "running" + self._reader = threading.Thread( + target=self._read_loop, name=f"{self.job_id}-reader", daemon=True) + self._reader.start() + + def stop(self) -> bool: + with self._lock: + process = self.process + if process is None or process.poll() is not None: + return False + try: + os.killpg(os.getpgid(process.pid), signal.SIGTERM) + except (ProcessLookupError, PermissionError): + process.terminate() + self.status = "stopping" + return True + + def _read_loop(self) -> None: + process = self.process + assert process is not None and process.stdout is not None + try: + for raw in process.stdout: + line = raw.rstrip("\n") + self._on_line(line) + process.wait() + except Exception as exc: # pragma: no cover - 子进程读取异常 + with self._lock: + self.error = f"{type(exc).__name__}: {exc}" + finally: + with self._lock: + self.finished_at = time.time() + if self.returncode is None: + self.returncode = process.poll() + if self.status == "stopping": + self.status = "stopped" + elif self.returncode == 0: + self.status = "exited" + else: + self.status = "failed" + self._flush_table() + # 最后半张表（Ctrl+C / 崩溃时没打印 total_timesteps）按末尾 + # 已知刻度兜底提交，宁可标注来源也不丢最后一段曲线 + self._flush_pending_with_fallback() + + def _on_line(self, line: str) -> None: + with self._lock: + self.logs.append(line) + self._parse_progress_json(line) + self._parse_sb3_table(line) + self._refresh_monitor() + + # ------------------------------------------------------------ parsers + def _parse_progress_json(self, line: str) -> None: + stripped = line.strip() + if not stripped.startswith("{") or "progress" not in stripped: + return + try: + payload = json.loads(stripped) + except ValueError: + return + if not isinstance(payload, dict) or payload.get("event") != "progress": + return + point = {_normalize_metric(str(key)): value + for key, value in payload.items() if key != "event"} + steps = _finite_float(point.get("timesteps")) + if steps is None: + steps = _finite_float(point.get("total_timesteps")) + if steps is not None: + with self._lock: + self._last_json_steps = steps + self._append_metric(point) + + def _parse_sb3_table(self, line: str) -> None: + """兼容 SB3 verbose=1 的 ``| key | value |`` 表格。 + + SB3 每张表按 rollout/ → time/ → train/ → system/ 分段，``total_timesteps`` + 出现在 time/ 段——也就是要等同一张表的 rollout/ 行先读进来之后才拿到 + 横轴。因此不能在段头立刻提交：那会把还没有刻度的 rollout/ 行丢掉，或 + 把它们算到上一张表的刻度上。 + + 规则： + - 每次"回到 rollout/ 段"视为新一张表开始，先把上一张已封闭的表提交； + - 表格分隔线表示本表结束，此时才允许用上一张表的刻度兜底（截断表）； + - 从未封闭过的半张表一直挂到进程结束才兜底提交，避免用旧刻度冒充。 + """ + if not line.startswith("|"): + if line.startswith("-"): + self._flush_table() + return + cells = [cell.strip() for cell in line.split("|")] + if len(cells) < 4: + return + raw_key, raw_value = cells[1], cells[2] + if raw_key in _SB3_SECTIONS: + if cells[0] != "": + return + with self._lock: + if raw_key == _SB3_SECTIONS[0] and self._table_sections: + self._flush_table() + if not self._table_sections or self._table_sections[-1][0] != raw_key: + self._table_sections.append((raw_key, {})) + return + key = _normalize_metric(raw_key) + value = _finite_float(raw_value) + if not key or value is None: + return + with self._lock: + if not self._table_sections: + self._table_sections.append((_SB3_SECTIONS[0], {})) + self._table_sections[-1][1][key] = value + if key == "total_timesteps": + self._table_steps = value + + def _flush_table(self) -> None: + """提交缓存中的 SB3 表。 + + 刻度优先级：本表的 ``total_timesteps`` > 上一张表的已知刻度（仅在遇到 + 分隔线即"确认整表结束"时才允许兜底，见 ``_on_line``）。完全没有刻度 + 就丢弃——伪造横轴会在曲线上画出重复的幽灵点。与 JSON 进度行同一步数时 + 跳过表格版本（JSON 字段更全），表格只用于补 clip_fraction/learning_rate。 + """ + with self._lock: + sections = self._table_sections + steps = self._table_steps + self._table_sections = [] + self._table_steps = None + last_json = self._last_json_steps + if not sections: + return + table: dict[str, Any] = {} + for _name, items in sections: + table.update(items) + if steps is None: + steps = _finite_float(table.get("total_timesteps")) + if steps is None: + # 半张表：交给调用方决定是否已确认结束（终止时按末尾兜底提交） + with self._lock: + self._pending_table = table + return + with self._lock: + self._last_table_steps = steps + if last_json is not None: + tolerance = max(1.0, 0.25 * steps / max(1.0, self.config.rollout_steps)) + if abs(steps - last_json) <= tolerance: + return + if "timesteps" not in table: + table["timesteps"] = steps + self._append_metric(table) + + def _flush_pending_with_fallback(self) -> None: + """进程结束时提交最后半张表：用上一次已知刻度兜底，避免丢数据。""" + with self._lock: + pending = self._pending_table + self._pending_table = None + steps = self._last_table_steps + if not pending or steps is None: + return + if "timesteps" not in pending: + pending = {**pending, "timesteps": steps} + self._append_metric(pending) + + def _append_metric(self, point: dict[str, Any]) -> None: + clean: dict[str, Any] = {} + for key, value in point.items(): + number = _finite_float(value) + clean[key] = number if number is not None else value + # 补齐 timesteps：progress JSON 优先，SB3 表用 total_timesteps。 + if "timesteps" not in clean and "total_timesteps" in clean: + clean["timesteps"] = clean["total_timesteps"] + with self._lock: + if self.metrics and clean == self.metrics[-1]: + return + self.metrics.append(clean) + + def _refresh_monitor(self) -> None: + """训练脚本写入的 Monitor CSV：作为逐 episode 奖励的备用来源。""" + parent = self.config.model_path.parent + files = sorted(parent.glob("high_curve_*_env*.monitor.csv")) \ + if parent.is_dir() else [] + if not files: + return + signature = (len(files), max(path.stat().st_mtime_ns for path in files)) + if signature == self._last_monitor_sig: + return + rows: list[dict[str, Any]] = [] + for path in files: + try: + lines = path.read_text(encoding="utf-8", errors="replace").splitlines() + except OSError: + continue + header: list[str] | None = None + for line in lines: + if line.startswith("#") or not line.strip(): + continue + if header is None: + header = [item.strip() for item in line.split(",")] + continue + values = line.split(",") + if len(values) < len(header): + continue + row = {} + valid = True + for key, raw in zip(header, values): + number = _finite_float(raw) + if number is None: + valid = False + break + row[key] = number + if valid: + rows.append(row) + with self._lock: + self.monitor = rows[-MAX_METRICS:] + self._last_monitor_sig = signature + + # ------------------------------------------------------------ snapshot + def snapshot(self, *, log_tail: int = 400, metric_limit: int = 2000, + after_metric: int | None = None) -> dict[str, Any]: + with self._lock: + now = self.finished_at or time.time() + elapsed = (now - self.started_at) if self.started_at else 0.0 + logs = list(self.logs)[-max(1, int(log_tail)):] + total = len(self.metrics) + if after_metric is None: + metrics = list(self.metrics)[-max(1, int(metric_limit)):] + offset = total - len(metrics) + else: + start = max(0, min(int(after_metric), total)) + metrics = list(self.metrics)[start:] + offset = start + # metric_end 是本批点在序列中的绝对末端下标：服务端 deque 满 5000 + # 后会丢弃最老的点，客户端只有拿到这个值才能区分"续接"与"服务端 + # 已滑动窗口"，否则同一批点会被重复追加。 + metric_end = offset + len(metrics) + monitor = self.monitor[-max(1, int(metric_limit)):] + config = dict(self.config.__dict__) + command = list(self.command) + model_path = str(self.config.model_path) + timesteps = int(self.config.timesteps) + payload = { + "job_id": self.job_id, + "status": self.status, + "pid": _pid(self.process), + "created_at": self.created_at, + "started_at": self.started_at, + "finished_at": self.finished_at, + "elapsed_s": round(elapsed, 3), + "returncode": self.returncode, + "error": self.error, + "command": command, + "model": model_path, + "config": config, + "log_lines": logs, + "log_total": len(self.logs), + "metrics": metrics, + "metric_offset": offset, + "metric_end": metric_end, + "metric_total": total, + "monitor": monitor, + } + payload["convergence"] = summarize_convergence(metrics, monitor) + payload["process"] = describe_process(payload) + return payload + + +def _series(metrics: list[dict[str, Any]], key: str) -> list[tuple[float, float]]: + points: list[tuple[float, float]] = [] + for point in metrics: + value = _finite_float(point.get(key)) + steps = _finite_float(point.get("timesteps")) or _finite_float( + point.get("total_timesteps")) + if value is None: + continue + points.append((steps if steps is not None else float(len(points) + 1), value)) + return points + + +def _last_value(points: list[tuple[float, float]]) -> float | None: + return points[-1][1] if points else None + + +def _spread(points: list[tuple[float, float]], window: int = 10 + ) -> tuple[float, float] | None: + """最近 window 个点的 (极差, 起点绝对值)，用于判断"是否还在动"。""" + sample = points[-window:] + if len(sample) < 3: + return None + ys = [item[1] for item in sample] + return max(ys) - min(ys), abs(ys[0]) + + +def _cv(points: list[tuple[float, float]], window: int = 10) -> float | None: + """最近 window 个点的变异系数（标准差/|均值|），越小越平稳。""" + sample = [item[1] for item in points[-window:]] + if len(sample) < 3: + return None + mean = sum(sample) / len(sample) + if abs(mean) < 1e-12: + return None + var = sum((value - mean) ** 2 for value in sample) / len(sample) + return (var ** 0.5) / abs(mean) + + +def _slope(points: list[tuple[float, float]], window: int = 8) -> float | None: + sample = points[-window:] + if len(sample) < 3: + return None + xs = [item[0] for item in sample] + ys = [item[1] for item in sample] + n = len(xs) + mean_x = sum(xs) / n + mean_y = sum(ys) / n + denom = sum((x - mean_x) ** 2 for x in xs) + if abs(denom) < 1e-12: + return None + return sum((x - mean_x) * (y - mean_y) for x, y in zip(xs, ys)) / denom + + +def _rate_per_1k(reward_points: list[tuple[float, float]], + window: int = 8) -> float | None: + """最近 window 个点的奖励增益换算成"每 1000 环境步"，便于读趋势。""" + slope = _slope(reward_points, window) + if slope is None: + return None + return slope * 1000.0 + + +def _fmt_num(value: float | None, digits: int = 4) -> str: + if value is None: + return "—" + if value != 0 and abs(value) < 1e-3: + return f"{value:.3e}" + return f"{value:.{digits}g}" + + +def _flatness(points: list[tuple[float, float]], window: int = 10 + ) -> tuple[float, float] | None: + """窗口内"趋势位移 / 逐点噪声"比值：<1 表示看不出系统性变化。 + + 判定收敛时只看斜率绝对值会把"很慢但持续的提升"误判为平台；这里把窗口 + 对半切，用两半均值之差当趋势位移，用相邻点平均绝对差当噪声尺度。返回 + (比值, 噪声尺度)，两者都小才是真的"又平又稳"。 + """ + sample = points[-window:] + if len(sample) < 4: + return None + ys = [item[1] for item in sample] + half = len(ys) // 2 + shift = abs(sum(ys[half:]) / len(ys[half:]) - sum(ys[:half]) / len(ys[:half])) + noise = sum(abs(b - a) for a, b in zip(ys, ys[1:])) / (len(ys) - 1) + if noise <= 0: + return (0.0, 0.0) if shift <= 0 else (float("inf"), 0.0) + return shift / noise, noise + + +def summarize_convergence(metrics: list[dict[str, Any]], + monitor: list[dict[str, Any]]) -> dict[str, Any]: + """给前端返回可解释的收敛状态与各参数最近值。 + + 这是**启发式判读**，不替代正式学习曲线分析：只依据最近窗口的斜率、 + 极差与变异系数给出"平坦/上升/未上升"的分类，并把同一组数字交给前端 + 展示，便于人工复核。 + """ + reward_points = _series(metrics, "ep_rew_mean") + if not reward_points and monitor: + reward_points = [(float(index), float(row.get("r", 0.0))) + for index, row in enumerate(monitor) + if _finite_float(row.get("r")) is not None] + kl_points = _series(metrics, "approx_kl") + ev_points = _series(metrics, "explained_variance") + vf_points = _series(metrics, "value_loss") + pg_points = _series(metrics, "policy_gradient_loss") + entropy_points = _series(metrics, "entropy_loss") + fps_points = _series(metrics, "fps") + + reward_slope = _slope(reward_points) + reward_rate = _rate_per_1k(reward_points) + spread = _spread(reward_points) + cv = _cv(reward_points) + latest_kl = _last_value(kl_points) + latest_ev = _last_value(ev_points) + latest_vf = _last_value(vf_points) + latest_pg = _last_value(pg_points) + latest_entropy = _last_value(entropy_points) + latest_fps = _last_value(fps_points) + latest_timesteps = metrics[-1].get("timesteps") if metrics else None + if latest_timesteps is None and metrics: + latest_timesteps = metrics[-1].get("total_timesteps") + latest_reward = _last_value(reward_points) + latest_len = _last_value(_series(metrics, "ep_len_mean")) + latest_updates = _last_value(_series(metrics, "n_updates")) + window = min(10, max(3, len(reward_points))) + reward_window = [round(value, 6) for _, value in reward_points[-window:]] + best_reward = max((value for _, value in reward_points), default=None) + + notes: list[str] = [] + if not metrics and not monitor: + status, tone = "等待首个 rollout", "gray" + notes.append("训练进程启动后先输出环境初始化与首个 rollout 的进度行。") + elif not metrics: + status, tone = "已收到回合奖励，等待进度行", "warn" + notes.append("Monitor CSV 已有回合记录，但尚未收到 --progress-json 进度行；" + "首个 rollout 结束后会出现完整指标。") + elif len(metrics) < 3: + status, tone = "预热阶段", "warn" + notes.append("已收到初始指标，需要更多 rollout 才能判断趋势。") + else: + tone, status = "ok", "收敛趋势良好" + # 平台判据（三条同时成立）：对半窗口看不出位移、窗口内波动小、 + # 逐点噪声本身也小。慢速但持续的提升会因第一条被排除。 + flat = _flatness(reward_points) + plateau = False + if flat is not None and cv is not None and latest_reward is not None: + flat_ratio, flat_noise = flat + scale = max(abs(latest_reward), 1e-9) + plateau = (flat_ratio <= 1.0 and cv <= 0.15 + and flat_noise <= 0.02 * scale + and (spread is None or spread[1] < 1e-12 + or spread[0] <= 0.15 * spread[1])) + if latest_kl is not None and latest_kl > 0.05: + status, tone = "KL 偏大，策略更新可能过猛", "bad" + notes.append(f"approx_kl={latest_kl:.4f} > 0.05") + elif latest_ev is not None and latest_ev < 0.0: + status, tone = "价值函数尚未拟合", "warn" + notes.append(f"explained_variance={latest_ev:.3f} < 0") + elif reward_slope is not None and reward_slope < 0: + status, tone = "奖励未形成上升趋势", "warn" + notes.append(f"最近 {window} 点奖励斜率为负（{_fmt_num(reward_slope)}/step）") + elif plateau: + status, tone = "已趋于平稳（疑似收敛）", "ok" + notes.append("窗口内对半位移小于逐点噪声，且波动很小" + f"（变异系数 {_fmt_num(cv, 3)}）") + else: + notes.append(f"最近 {window} 点奖励斜率为正（{_fmt_num(reward_slope)}/step）") + if latest_kl is not None and latest_kl <= 0.02: + notes.append(f"approx_kl={latest_kl:.4f} 较稳定") + if latest_ev is not None: + notes.append(f"explained_variance={latest_ev:.3f}") + if cv is not None: + notes.append(f"奖励变异系数={_fmt_num(cv, 3)}") + if latest_entropy is not None and abs(latest_entropy) < 1e-3: + notes.append("策略熵接近 0，探索可能过早停止") + return { + "status": status, + "tone": tone, + "notes": notes, + "window": window, + "latest_timesteps": latest_timesteps, + "reward_slope": reward_slope, + "reward_rate_per_1k": reward_rate, + "reward_cv": cv, + "reward_spread": spread[0] if spread else None, + "reward_window": reward_window, + "best_reward": best_reward, + "latest_reward": latest_reward, + "latest_ep_len": latest_len, + "latest_updates": latest_updates, + "latest_kl": latest_kl, + "latest_explained_variance": latest_ev, + "latest_value_loss": latest_vf, + "latest_policy_loss": latest_pg, + "latest_entropy_loss": latest_entropy, + "latest_fps": latest_fps, + "reward_points": len(reward_points), + } + + +# 快照状态 -> 界面展示用的进程语义（阶段名称、色调、是否终态） +_STATUS_VIEW = { + "created": ("已创建", "gray", False, "命令已构造，等待 spawn"), + "running": ("运行中", "ok", False, "子进程持有控制台输出与独立进程组"), + "stopping": ("停止中", "warn", False, "已发送 SIGTERM，等待子进程退出"), + "stopped": ("已手动停止", "warn", True, "收到停止信号后退出，模型可能未保存"), + "exited": ("正常结束", "ok", True, "退出码 0：训练与评估流程走完"), + "failed": ("异常结束", "bad", True, "非零退出码或启动失败，见日志尾部"), +} + +_PHASES = [ + ("created", "构造命令"), + ("spawn", "启动子进程"), + ("rollout", "采集 rollout"), + ("update", "PPO 更新"), + ("evaluate", "评估 / 保存"), + ("done", "收敛归档"), +] + +# 状态 -> 当前所处的阶段节点；节点之前为已完成，之后为未开始。 +_STATUS_PHASE = { + "created": "created", + "running": "update", + "stopping": "update", + "stopped": "done", + "exited": "done", + "failed": "done", +} + + +def _pid(process: subprocess.Popen | None) -> int | None: + """已退出进程的 pid 访问会抛 OSError；这里统一吞掉，只用于展示。""" + if process is None: + return None + try: + return int(process.pid) + except (OSError, ValueError): + return None + + +def describe_process(snapshot: dict[str, Any]) -> dict[str, Any]: + """把 snapshot 派生为界面直接可渲染的进程对象。 + + 包含：状态语义词、进度（步/百分比）、墙钟与剩余时间估计、最近吞吐、 + 进程时间线节点。数字都取自 snapshot，前端不再重复累加，避免两个刷新 + 频率不同的计数器互相打架。 + """ + status = str(snapshot.get("status") or "created") + label, tone, terminal, hint = _STATUS_VIEW.get( + status, (status, "gray", False, "")) + config = snapshot.get("config") or {} + order = [key for key, _ in _PHASES] + current = _STATUS_PHASE.get(status, "created") + timeline = [] + for key, title in _PHASES: + index = order.index(key) + if index < order.index(current): + state = "done" + elif index == order.index(current): + state = "active" + else: + state = "pending" + timeline.append({"key": key, "label": title, "state": state}) + + metrics = snapshot.get("metrics") or [] + latest: dict[str, Any] = metrics[-1] if metrics else {} + convergence = snapshot.get("convergence") or {} + total_steps = _finite_float(config.get("timesteps")) or 0.0 + # SB3 表格行可能没有 steps 字段，因此进度取"所有进度点的最大步数"， + # 而不是只看最后一个点——否则末行恰好缺字段时进度会回退到 0。 + done_steps = 0.0 + for point in metrics: + value = (_finite_float(point.get("timesteps")) + or _finite_float(point.get("total_timesteps"))) + if value is not None and value > done_steps: + done_steps = value + if done_steps == 0.0: + done_steps = _finite_float(convergence.get("latest_timesteps")) or 0.0 + percent = (100.0 * done_steps / total_steps) if total_steps > 0 else None + if percent is not None: + percent = max(0.0, min(100.0, percent)) + elapsed = float(snapshot.get("elapsed_s") or 0.0) + rate = _latest_rate(metrics) + remaining: float | None = None + eta_ts: float | None = None + if rate and rate > 0 and total_steps > 0 and done_steps < total_steps: + remaining = (total_steps - done_steps) / rate + eta_ts = float(snapshot.get("finished_at") or time.time()) + remaining + if not terminal and remaining is None: + cap = _finite_float(config.get("max_wall_s")) or 0.0 + if cap > 0: + remaining = max(0.0, cap - elapsed) + return { + "label": label, + "tone": tone, + "terminal": terminal, + "hint": hint, + "phase": current, + "timeline": timeline, + "percent": percent, + "done_steps": done_steps, + "total_steps": total_steps, + "elapsed_s": round(elapsed, 3), + "rate_per_s": rate, + "remaining_s": remaining, + "eta_epoch_s": eta_ts, + "log_total": snapshot.get("log_total"), + "log_capacity": MAX_LOG_LINES, + "metric_total": snapshot.get("metric_total"), + "latest": latest, + } + + +def _latest_rate(metrics: list[dict[str, Any]], window: int = 6) -> float | None: + """最近若干个**带 fps 的**进度点的平均吞吐（步/秒），用于剩余时间估计。 + + progress JSON 行不含 fps，只有 SB3 表格行有；若只看最后 window 个点， + 恰好落在若干 JSON 行之间时会得到 None，剩余时间就会退化成墙钟上限的 + 粗糙估计。这里向前扫描直到拿到至少 1 个 fps（最多看 3*window 个点）。 + """ + if not metrics: + return None + usable: list[float] = [] + for point in reversed(metrics[-window * 3:]): + value = _finite_float(point.get("fps")) + if value and value > 0: + usable.append(value) + if len(usable) >= window: + break + if not usable: + return None + return sum(usable) / len(usable) + + +class TrainingUI: + """训练前端管理器：持有唯一 TrainingJob 与 HTTP 服务器。""" + + def __init__(self, ui_port: int = 8036): + self.ui_port = int(ui_port) + self.job: TrainingJob | None = None + self._lock = threading.RLock() + try: + self.httpd = ThreadingHTTPServer(("127.0.0.1", self.ui_port), _UIHandler) + except OSError as exc: + raise OSError( + f"无法在 127.0.0.1:{self.ui_port} 启动训练前端（{exc}）；" + f"换一个 --ui-port 或先结束占用该端口的进程") from exc + self.httpd.daemon_threads = True + self.httpd.training_ui = self + self._serving = False + + # ------------------------------------------------------------ API + def start(self, payload: dict[str, Any]) -> dict[str, Any]: + config = TrainConfig.from_payload(payload) + with self._lock: + if self.job is not None and self.job.status in ( + "created", "running", "stopping"): + raise RuntimeError("已有训练任务在运行，请先停止") + self.job = TrainingJob(config) + try: + self.job.start() + except Exception: + self.job.status = "failed" + self.job.error = "训练进程启动失败" + raise + # 与 snapshot() 同形：HTTP 层直接把返回值当响应体，不再二次拆包 + return {"job": self.job.snapshot(log_tail=1)} + + def stop(self) -> bool: + with self._lock: + return bool(self.job and self.job.stop()) + + def snapshot(self, *, log_tail: int, metric_limit: int, + after_metric: int | None = None) -> dict[str, Any]: + with self._lock: + if self.job is None: + return {"job": None, "message": "尚未启动训练"} + return {"job": self.job.snapshot(log_tail=log_tail, + metric_limit=metric_limit, + after_metric=after_metric)} + + @property + def is_serving(self) -> bool: + return self._serving + + def serve_forever(self) -> None: + self._serving = True + try: + self.httpd.serve_forever() + finally: + self._serving = False + + def shutdown(self) -> None: + self.stop() + if self._serving: + self.httpd.shutdown() + self.httpd.server_close() + + +class _UIHandler(BaseHTTPRequestHandler): + protocol_version = "HTTP/1.1" + server_version = "MCM26B-Training-UI/1.0" + + def log_message(self, format, *args): # noqa: A002 + pass + + @property + def ui(self) -> TrainingUI: + return self.server.training_ui # type: ignore[attr-defined] + + def do_GET(self): + parts = urlsplit(self.path) + if parts.path in STATIC_FILES: + self._serve_static(parts.path) + elif parts.path == "/api/train/schema": + self._send_json(200, config_schema()) + elif parts.path == "/api/train/state": + params = parse_qs(parts.query) + if (params.get("preview") or ["0"])[0] in ("1", "true", "yes"): + # 离线预览：返回合成快照，不触碰任何训练进程 + scenario = (params.get("scenario") or ["rising"])[0] + if scenario not in DEMO_SCENARIOS: + self._send_json(400, {"error": f"未知预览场景 {scenario}"}) + return + self._send_json(200, {"job": demo_snapshot(log_tail=400, + scenario=scenario)}) + return + try: + log_tail = _int_query(params, "log_tail", 400, 1, MAX_LOG_LINES) + metric_limit = _int_query(params, "metric_limit", 2000, 1, + MAX_METRICS) + after = _int_query(params, "after_metric", None, 0, + MAX_METRICS * 2, optional=True) + except ValueError as exc: + self._send_json(400, {"error": str(exc)}) + return + self._send_json(200, self.ui.snapshot( + log_tail=log_tail, metric_limit=metric_limit, + after_metric=after)) + else: + self._send_json(404, {"error": f"未知路径 {parts.path}"}) + + def do_POST(self): + parts = urlsplit(self.path) + payload = self._read_json() + if payload is None: + self._send_json(400, {"error": "请求体必须是合法 JSON"}) + return + if not isinstance(payload, dict): + self._send_json(400, {"error": "请求体必须是 JSON 对象"}) + return + try: + if parts.path == "/api/train/start": + self._send_json(200, self.ui.start(payload)) + elif parts.path == "/api/train/stop": + self._send_json(200, {"stopped": self.ui.stop()}) + else: + self._send_json(404, {"error": f"未知路径 {parts.path}"}) + except (ValueError, RuntimeError, FileNotFoundError, + PermissionError) as exc: + self._send_json(409, {"error": str(exc)}) + except OSError as exc: + self._send_json(409, {"error": f"操作系统错误：{exc}"}) + except Exception as exc: # pragma: no cover - 兜底，避免连接被静默中断 + self._send_json(500, {"error": f"服务端内部错误：{type(exc).__name__}: {exc}"}) + + def _serve_static(self, path: str) -> None: + filename, content_type = STATIC_FILES[path] + data = (STATIC_DIR / filename).read_bytes() + self.send_response(200) + self.send_header("Content-Type", content_type) + self.send_header("Content-Length", str(len(data))) + self.send_header("Cache-Control", "no-store") + self.end_headers() + self.wfile.write(data) + + def _read_json(self) -> dict[str, Any] | None: + try: + length = int(self.headers.get("Content-Length") or 0) + except ValueError: + return None + if length <= 0 or length > 1_000_000: + return {} + data = self.rfile.read(length) + try: + return json.loads(data.decode("utf-8")) + except (ValueError, UnicodeDecodeError): + return None + + def _send_json(self, status: int, payload: dict[str, Any]) -> None: + data = json.dumps(payload, ensure_ascii=False, allow_nan=False).encode("utf-8") + self.send_response(status) + self.send_header("Content-Type", "application/json; charset=utf-8") + self.send_header("Content-Length", str(len(data))) + self.send_header("Cache-Control", "no-store") + self.end_headers() + self.wfile.write(data) + + +def run_training_ui(argv=None) -> int: + import argparse + + parser = argparse.ArgumentParser(description="HRL 模型训练前端（本地回环）") + parser.add_argument("--ui-port", type=int, default=8036) + parser.add_argument("--no-browser", action="store_true") + parser.add_argument("--print-schema", action="store_true", + help="打印配置契约（字段/分组/预设/指标）后退出") + parser.add_argument("--print-preview", action="store_true", + help="打印离线示例快照 JSON 后退出") + args = parser.parse_args(argv) + if args.print_schema: + print(json.dumps(config_schema(), ensure_ascii=False, indent=2)) + return 0 + if args.print_preview: + print(json.dumps({"job": demo_snapshot(log_tail=200)}, + ensure_ascii=False, indent=2)) + return 0 + try: + ui = TrainingUI(ui_port=args.ui_port) + except OSError as exc: + print(f"[训练前端] {exc}", file=sys.stderr) + return 2 + url = f"http://127.0.0.1:{args.ui_port}/" + # flush=True：界面常用 `> log 2>&1 &` 方式后台启动，不刷新会看不到启动横幅 + print(f"训练前端已启动：{url}", flush=True) + print(f" 配置契约：{url}api/train/schema", flush=True) + print(f" 状态快照：{url}api/train/state", flush=True) + print(f" 离线预览：{url}api/train/state?preview=1", flush=True) + print(" 界面只监听 127.0.0.1；Ctrl+C 退出并停止训练子进程。", flush=True) + if not args.no_browser: + try: + import webbrowser + webbrowser.open(url) + except Exception: + pass + try: + ui.serve_forever() + except KeyboardInterrupt: + pass + finally: + ui.shutdown() + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(run_training_ui()) diff --git a/src/mcm26b/training_ui/static/app.js b/src/mcm26b/training_ui/static/app.js new file mode 100644 index 0000000..bc1bf48 --- /dev/null +++ b/src/mcm26b/training_ui/static/app.js @@ -0,0 +1,756 @@ +/* 训练前端主逻辑：纯原生 JS，无外部依赖。 + 职责分工： + - 表单由 /api/train/schema 的字段元数据生成，不在前端复制校验规则； + - 训练数值全部取自 /api/train/state 快照（服务端已算好进度、吞吐、 + 收敛判读），前端只做格式化与渲染，避免两套算法对不上； + - 指标增量拉取（after_metric 游标），日志按尾部行数拉取。 */ +"use strict"; + +const $ = (id) => document.getElementById(id); +const REFRESH_MS = 1500; +// 与服务端 MAX_METRICS 对齐：本地缓存上限，避免长时间训练把内存吃满 +const METRIC_LIMIT = 5000; + +const state = { + schema: null, + preview: false, // 离线预览：不再启动进程，只渲染示例快照 + polling: false, + saving: false, // 配置表单是否处于"训练中"锁定态 + metrics: [], // 本地累积的进度点（按 timesteps 递增） + metricCache: [], // metrics 的同一个数组（显式持有，避免 slice/concat 时丢引用） + metricNext: 0, // 本地已吃到的下一个下标（= 服务端下次应返回的 offset） + metricTotal: 0, // 服务端累计进度点总数（增量拉取的游标） + chartKey: "ep_rew_mean", + jobKey: null, // job_id 变化时清空本地缓存 + lastLogSignature: "", +}; + +// ---------------------------------------------------------------- 工具 +function escapeHtml(value) { + return String(value == null ? "" : value) + .replace(/[&<>"']/g, (c) => ({ "&": "&", "<": "<", ">": ">", + '"': """, "'": "'" }[c])); +} +function fmtNumber(value, digits) { + if (value === null || value === undefined || value === "" || Number.isNaN(Number(value))) return "—"; + const num = Number(value); + const abs = Math.abs(num); + if (abs !== 0 && (abs < 1e-3 || abs >= 1e5)) return num.toExponential(2); + const d = digits === undefined ? (abs >= 100 ? 1 : abs >= 1 ? 2 : 4) : digits; + return num.toFixed(d); +} +function fmtInt(value) { + if (value === null || value === undefined || Number.isNaN(Number(value))) return "—"; + return Math.round(Number(value)).toLocaleString("en-US"); +} +function fmtDuration(seconds) { + if (seconds === null || seconds === undefined || !isFinite(seconds) || seconds < 0) return "—"; + const s = Math.round(seconds); + const h = Math.floor(s / 3600), m = Math.floor((s % 3600) / 60), sec = s % 60; + if (h > 0) return `${h}h${String(m).padStart(2, "0")}m`; + if (m > 0) return `${m}m${String(sec).padStart(2, "0")}s`; + return `${sec}s`; +} +function fmtClock(epochSeconds) { + if (!epochSeconds) return "—"; + const d = new Date(epochSeconds * 1000); + return `${String(d.getHours()).padStart(2, "0")}:${String(d.getMinutes()).padStart(2, "0")}:${String(d.getSeconds()).padStart(2, "0")}`; +} +function chip(id, label, value, tone) { + const el = $(id); + if (!el) return; + el.className = `chip ${tone || "gray"}`; + el.innerHTML = `${label}${escapeHtml(value)}`; +} +function setText(id, text) { const el = $(id); if (el) el.textContent = text; } + +// ---------------------------------------------------------------- 启动 +async function boot() { + try { + const res = await fetch("/api/train/schema"); + if (!res.ok) throw new Error(`HTTP ${res.status}`); + state.schema = await res.json(); + } catch (err) { + state.schema = { fields: [], groups: [], presets: [], metrics: [] }; + showBanner("bad", `无法读取配置契约（${err.message}）：请确认已通过 python -m mcm26b.training_ui 启动前端。`); + } + buildForm(); + await poll(); + bindChartInteractions(); + setInterval(poll, REFRESH_MS); +} + +function showBanner(tone, html) { + const el = $("banner"); + if (!html) { el.classList.add("hidden"); el.innerHTML = ""; return; } + el.className = `banner ${tone || ""}`; + el.innerHTML = html; + el.classList.remove("hidden"); +} + +// ---------------------------------------------------------------- 表单 +function buildForm() { + const presets = $("presets"); + presets.innerHTML = ""; + for (const preset of state.schema.presets || []) { + const btn = document.createElement("button"); + btn.type = "button"; + btn.className = "preset"; + btn.innerHTML = `${escapeHtml(preset.label)}${escapeHtml(preset.note || "")}`; + btn.onclick = () => { applyValues(preset.payload); $("form-error").textContent = ""; }; + presets.appendChild(btn); + } + + const wrap = $("form-groups"); + wrap.innerHTML = ""; + for (const group of state.schema.groups || []) { + const box = document.createElement("fieldset"); + box.className = "group"; + const fields = (state.schema.fields || []).filter((f) => f.group === group.key); + box.innerHTML = `${escapeHtml(group.label)}` + + `

${escapeHtml(group.hint || "")}

`; + for (const field of fields) box.appendChild(buildField(field)); + wrap.appendChild(box); + } + applyValues({}); +} + +function buildField(field) { + const row = document.createElement("div"); + row.className = "field"; + row.id = `field-${field.key}`; + const label = document.createElement("label"); + label.className = "flabel"; + label.innerHTML = `${escapeHtml(field.label)}` + + (field.unit ? `${escapeHtml(field.unit)}` : ""); + let control; + if (field.kind === "select") { + control = document.createElement("select"); + for (const choice of field.choices || []) { + const option = document.createElement("option"); + option.value = String(choice.value); + option.textContent = choice.label; + control.appendChild(option); + } + } else if (field.kind === "bool") { + control = document.createElement("input"); + control.type = "checkbox"; + row.classList.add("check"); + } else { + control = document.createElement("input"); + control.type = field.kind === "text" ? "text" : "number"; + if (field.kind === "number") control.step = field.step || "any"; + if (field.min !== undefined) control.min = field.min; + if (field.max !== undefined) control.max = field.max; + } + control.id = `cfg-${field.key}`; + control.dataset.key = field.key; + control.addEventListener("input", () => { row.classList.add("dirty"); row.classList.remove("invalid"); }); + row.appendChild(label); + row.appendChild(control); + if (field.help) { + const help = document.createElement("span"); + help.className = "fhelp"; + help.textContent = field.help; + row.appendChild(help); + } + return row; +} + +function setControl(field, value) { + const control = $(`cfg-${field.key}`); + if (!control) return; + if (field.kind === "bool") { control.checked = Boolean(value); return; } + if (value === null || value === undefined) { control.value = ""; return; } + control.value = String(value); +} + +function applyValues(values) { + for (const field of state.schema.fields || []) { + const has = Object.prototype.hasOwnProperty.call(values, field.key); + setControl(field, has ? values[field.key] : field.default); + const row = $(`field-${field.key}`); + if (row) row.classList.remove("dirty", "invalid"); + } +} + +function collectValues() { + const out = {}; + for (const field of state.schema.fields || []) { + const control = $(`cfg-${field.key}`); + if (!control) continue; + if (field.kind === "bool") { out[field.key] = control.checked; continue; } + const raw = String(control.value || "").trim(); + if (raw === "") { out[field.key] = null; continue; } + if (field.kind === "integer") out[field.key] = Number.parseInt(raw, 10); + else if (field.kind === "number") out[field.key] = Number.parseFloat(raw); + else if (field.kind === "select") { + const choices = field.choices || []; + const match = choices.find((c) => String(c.value) === raw); + out[field.key] = match ? match.value : raw; + } else out[field.key] = raw; + } + return out; +} + +/* 与后端 validate() 对齐的前置提示：只做提示，不阻止提交后端的权威判定 */ +function validateLocally(values) { + const problems = []; + for (const field of state.schema.fields || []) { + const value = values[field.key]; + if (value === null || value === undefined) continue; + if (field.kind === "integer" || field.kind === "number") { + if (!isFinite(value)) { problems.push(`${field.label} 不是有效数字`); markInvalid(field.key, true); continue; } + if (field.min !== undefined && value < field.min) { problems.push(`${field.label} 不能小于 ${field.min}`); markInvalid(field.key, true); continue; } + if (field.max !== undefined && value > field.max) { problems.push(`${field.label} 不能大于 ${field.max}`); markInvalid(field.key, true); continue; } + markInvalid(field.key, false); + } + } + if (values.rollout_steps && values.timesteps && values.rollout_steps > values.timesteps) { + problems.push("采样步数 n_steps 不能大于总步数 timesteps"); + markInvalid("rollout_steps", true); + } + if (values.batch_size && values.rollout_steps && values.envs + && values.batch_size > values.rollout_steps * values.envs) { + problems.push("批大小 batch_size 大于 n_steps × envs，PPO 每轮数据不足以分批"); + markInvalid("batch_size", true); + } + return problems; +} + +function markInvalid(key, bad) { + const row = $(`field-${key}`); + if (row) row.classList.toggle("invalid", Boolean(bad)); +} + +// ---------------------------------------------------------------- 轮询 +async function poll() { + if (state.polling) return; + if (state.preview) return; + state.polling = true; + try { + const query = new URLSearchParams({ + log_tail: $("log-tail").value || "400", + metric_limit: String(METRIC_LIMIT), + }); + if (state.metricTotal > 0) query.set("after_metric", String(state.metricTotal)); + const res = await fetch(`/api/train/state?${query.toString()}`); + const payload = await res.json(); + if (!res.ok) throw new Error(payload.error || `HTTP ${res.status}`); + chip("chip-conn", "连接", "已连接", "ok"); + render(payload.job); + } catch (err) { + chip("chip-conn", "连接", "断开", "bad"); + } finally { + state.polling = false; + } +} + +/* 合并一次快照返回的进度点。服务端用 metric_offset 给出这批点的绝对起点， + 客户端只需与"本地已吃到的下一个下标"（metricNext）对账： + - offset === metricNext：正常续接（增量拉取），concat； + - offset > metricNext：本地缓存与这批点之间缺了若干点（例如页面刷新后 + 服务端窗口已前移），保留本地旧点会让曲线出现空洞，因此整体替换； + - offset < metricNext：本地缓存比服务端更新（服务端重启/计数回退）， + 忽略这批过期点，不动本地缓存。 + 判据是"绝对下标"而不是长度：游标是服务端总数，本地可能被裁剪得更短， + 拿长度比较会把同一批点重复追加（本函数注释即为此 bug 的回归说明）。 + 下标必须显式转 Number：JSON 里若出现字符串形式的数字，JS 的 >= 会退化成 + 字典序比较（"14" >= "014" 为 false），静默走错分支。 */ +function absorbMetrics(job) { + const incoming = Array.isArray(job.metrics) ? job.metrics : []; + const rawOffset = Number(job.metric_offset); + const offset = Number.isFinite(rawOffset) ? rawOffset : null; + const cache = state.metricCache || []; + if (incoming.length && offset !== null) { + if (!cache.length || offset > state.metricNext) { + // 首批，或与本地之间有缺口（服务端窗口前移）：整体替换，避免曲线断档 + state.metricCache = incoming.slice(); + state.metricNext = offset + incoming.length; + } else if (offset === state.metricNext) { + state.metricCache = cache.concat(incoming); + state.metricNext = offset + incoming.length; + } else { + return; // 过期批次：本地已有更新的点，直接忽略 + } + } + if (state.metricCache.length > METRIC_LIMIT) { + state.metricCache = state.metricCache.slice(state.metricCache.length - METRIC_LIMIT); + } + state.metrics = state.metricCache; + const total = Number(job.metric_total); + if (Number.isFinite(total)) state.metricTotal = total; +} + +// ---------------------------------------------------------------- 渲染 +function render(job) { + if (!job) { + renderIdle(); + return; + } + if (job.job_id !== state.jobKey) { + state.jobKey = job.job_id; + state.metrics = []; + state.metricTotal = 0; + state.lastLogSignature = ""; + } + absorbMetrics(job); + + renderProcess(job); + renderConvergence(job); + renderCharts(); + renderLog(job); + renderEpisodes(job); + renderBanner(job); + renderJobLine(job); +} + +function renderIdle() { + chip("chip-process", "进程", "未启动", "gray"); + chip("chip-progress", "进度", "—", "gray"); + chip("chip-elapsed", "墙钟", "—", "gray"); + chip("chip-device", "设备", "—", "gray"); + setText("process-hint", "启动任务后显示子进程与步进进度"); + setText("steps-done", "0"); setText("steps-total", "0"); + setText("progress-percent", "0.0%"); + $("bar-fill").style.width = "0%"; + setText("eta", "—"); setText("eta-clock", "—"); setText("metric-total", "0"); + for (const id of ["stat-pid", "stat-fps", "stat-elapsed", "stat-rc"]) setText(id, "—"); + setText("stat-logs", "0"); + $("timeline").innerHTML = ""; + setText("command-preview", ""); + chip("chip-converge", "判读", "等待数据", "gray"); + $("converge-notes").innerHTML = ""; + $("metric-grid").innerHTML = '

尚未启动训练，暂无参数。

'; + setFormLocked(false); +} + +function renderJobLine(job) { + const cfg = job.config || {}; + const parts = [ + `任务 ${job.job_id}`, `问题 ${cfg.problem}`, `阶段 ${cfg.stage}`, + `口径 ${cfg.reward_profile}/${cfg.config_profile}`, + `seed ${cfg.seed}`, `envs ${cfg.envs}·${cfg.vec_env}`, + ]; + setText("job-line", parts.join(" · ")); + chip("chip-device", "设备", `${cfg.device || "?"}`, "cyan"); +} + +function renderProcess(job) { + const process = job.process || {}; + const tone = process.tone || "gray"; + chip("chip-process", "进程", process.label || job.status, tone); + chip("chip-elapsed", "墙钟", fmtDuration(process.elapsed_s), tone === "gray" ? "gray" : "cyan"); + + const percent = process.percent; + setText("chip-progress-value", ""); + chip("chip-progress", "进度", + percent === null || percent === undefined ? "—" : `${percent.toFixed(1)}%`, + percent !== null && percent >= 100 ? "ok" : "cyan"); + setText("steps-done", fmtInt(process.done_steps)); + setText("steps-total", fmtInt(process.total_steps)); + setText("progress-percent", percent === null || percent === undefined ? "—" : `${percent.toFixed(2)}%`); + const fill = $("bar-fill"); + fill.style.width = `${percent === null || percent === undefined ? 0 : percent}%`; + fill.className = "bar-fill" + (tone === "bad" ? " bad" : tone === "warn" ? " warn" : tone === "ok" && percent >= 100 ? " ok" : ""); + setText("process-hint", process.hint || ""); + setText("eta", fmtDuration(process.remaining_s)); + setText("eta-clock", fmtClock(process.eta_epoch_s)); + setText("metric-total", fmtInt(process.metric_total)); + + setText("stat-pid", process.terminal ? "已退出" : (job.pid ? `PID ${job.pid}` : "—")); + setText("stat-pgid", process.terminal ? "已回收" : "独立会话"); + const fps = state.metrics.length ? lastFinite(state.metrics, "fps") : null; + setText("stat-fps", fps === null ? "—" : fmtNumber(fps, 1)); + setText("stat-elapsed", fmtDuration(process.elapsed_s)); + const rc = $("stat-rc"); + rc.textContent = job.returncode === null || job.returncode === undefined ? "—" : String(job.returncode); + rc.className = "v" + (job.returncode === 0 ? " ok" : job.returncode ? " bad" : ""); + setText("stat-logs", `${fmtInt(process.log_total)} / ${fmtInt(process.log_capacity)}`); + + renderTimeline(process); + setText("command-preview", job.command ? `$ ${job.command.join(" ")}` : ""); + setFormLocked(job.status === "running" || job.status === "created" || job.status === "stopping"); + $("btn-stop").disabled = !(job.status === "running" || job.status === "created"); +} + +function renderTimeline(process) { + const wrap = $("timeline"); + const nodes = process.timeline || []; + if (!nodes.length) { wrap.innerHTML = ""; return; } + const active = nodes.findIndex((n) => n.state === "active"); + if (wrap.dataset.signature !== nodes.map((n) => n.label).join("|")) { + wrap.innerHTML = ""; + nodes.forEach((node, index) => { + if (index > 0) { + const link = document.createElement("span"); + link.className = "link"; + link.dataset.link = String(index); + wrap.appendChild(link); + } + const el = document.createElement("span"); + el.className = "node"; + el.dataset.node = node.key; + el.innerHTML = `${escapeHtml(node.label)}`; + wrap.appendChild(el); + }); + wrap.dataset.signature = nodes.map((n) => n.label).join("|"); + } + nodes.forEach((node, index) => { + const el = wrap.querySelector(`[data-node="${node.key}"]`); + if (!el) return; + let cls = "node"; + if (node.state === "done") cls += " done"; + else if (node.state === "active") cls += " active"; + if (node.state === "active" && process.tone === "bad") cls += " failed"; + el.className = cls; + const link = wrap.querySelector(`[data-link="${index}"]`); + if (link) link.className = "link" + (index <= active ? " done" : ""); + }); +} + +function setFormLocked(locked) { + state.saving = locked; + for (const field of state.schema.fields || []) { + const control = $(`cfg-${field.key}`); + if (control) control.disabled = locked; + } + $("btn-start").disabled = locked; + $("btn-start").textContent = locked ? "训练中…" : "开始训练"; +} + +function renderBanner(job) { + const process = job.process || {}; + if (state.preview) { + showBanner("warn", "离线预览模式：正在渲染示例快照，不会启动训练进程。点「刷新」可返回实时轮询。"); + return; + } + if (job.status === "failed") { + showBanner("bad", `训练异常结束（退出码 ${job.returncode === null ? "?" : job.returncode}）：${escapeHtml(job.error || "见日志尾部")}。`); + } else if (job.status === "stopped") { + showBanner("warn", "已按请求停止训练（SIGTERM 独立进程组），本轮模型可能未保存。"); + } else if (job.status === "exited") { + showBanner("ok", "训练与评估流程正常结束，模型与元数据已写入输出目录。"); + } else if (process.remaining_s !== null && process.remaining_s !== undefined + && process.eta_epoch_s === null && job.status === "running") { + showBanner("warn", `尚未产出可估算吞吐的进度点，剩余时间按墙钟上限回退估计：${fmtDuration(process.remaining_s)}。`); + } else { + showBanner("", ""); + } +} + +// ---------------------------------------------------------------- 收敛 +function renderConvergence(job) { + const convergence = job.convergence || {}; + chip("chip-converge", "判读", convergence.status || "等待数据", convergence.tone || "gray"); + const notes = convergence.notes || []; + $("converge-notes").innerHTML = notes.length + ? notes.map((note) => `
  • ${escapeHtml(note)}
    • `).join("") + : "
  • 暂无判读说明。
    • "; + + const grid = $("metric-grid"); + grid.innerHTML = ""; + const catalog = state.schema.metrics || []; + for (const metric of catalog) { + const points = seriesOf(metric.key); + const values = points.map((p) => p[1]); + const value = values.length ? values[values.length - 1] : null; + const prev = values.length > 1 ? values[values.length - 2] : null; + const delta = value !== null && prev !== null ? value - prev : null; + const card = document.createElement("div"); + card.className = "metric-card"; + const alert = isAlert(metric.key, value); + if (alert) card.classList.add("alert"); + const trendClass = delta === null || Math.abs(delta) < 1e-12 ? "flat" + : delta > 0 ? "up" : "down"; + const arrow = delta === null || Math.abs(delta) < 1e-12 ? "→" : delta > 0 ? "▲" : "▼"; + card.innerHTML = + `
    ${escapeHtml(metric.label)}` + + `${escapeHtml(fmtValue(metric, value))}
    ` + + `` + + `
    ${arrow} ${escapeHtml(delta === null ? "—" : fmtValue(metric, delta))}` + + `${points.length} 点
    `; + if (alert) card.title = alert; + grid.appendChild(card); + const canvas = card.querySelector("canvas"); + if (canvas && values.length >= 2) { + window.Charts.sparkline(canvas, values.slice(-120), { color: metric.color }); + } + } +} + +function isAlert(key, value) { + if (value === null || value === undefined) return null; + if (key === "approx_kl" && value > 0.05) return "近似 KL 超过 0.05：策略更新幅度过大"; + if (key === "explained_variance" && value < 0) return "解释方差为负：价值函数尚未拟合"; + if (key === "entropy_loss" && Math.abs(value) < 1e-3) return "策略熵接近 0：探索可能过早停止"; + return null; +} + +function fmtValue(metric, value) { + if (value === null || value === undefined || !isFinite(value)) return "—"; + if (metric.format === "integer") return fmtInt(value); + return fmtNumber(value); +} + +function seriesOf(key) { + const points = []; + for (const row of state.metrics) { + const value = Number(row[key]); + if (!isFinite(value)) continue; + const x = Number(row.timesteps ?? row.total_timesteps ?? points.length + 1); + points.push([isFinite(x) ? x : points.length + 1, value]); + } + return points; +} +function lastFinite(rows, key) { + for (let i = rows.length - 1; i >= 0; i--) { + const value = Number(rows[i][key]); + if (isFinite(value)) return value; + } + return null; +} + +// ---------------------------------------------------------------- 图表 +function renderCharts() { + const catalog = state.schema.metrics || []; + const tabs = $("chart-tabs"); + if (tabs.dataset.signature !== catalog.map((m) => m.key).join("|")) { + tabs.innerHTML = ""; + for (const metric of catalog) { + const btn = document.createElement("button"); + btn.type = "button"; + btn.className = "chart-tab"; + btn.dataset.key = metric.key; + btn.textContent = metric.label; + btn.onclick = () => { state.chartKey = metric.key; renderCharts(); }; + tabs.appendChild(btn); + } + tabs.dataset.signature = catalog.map((m) => m.key).join("|"); + } + for (const btn of tabs.querySelectorAll(".chart-tab")) { + btn.classList.toggle("active", btn.dataset.key === state.chartKey); + } + + const windowSize = Number($("chart-window").value || 0); + const primary = catalog.find((m) => m.role === "primary") || { key: "ep_rew_mean", label: "奖励", color: "#4f8cff" }; + const rewardPoints = slice(seriesOf(primary.key), windowSize); + const layout = window.Charts.line($("chart-reward"), { + series: [{ name: primary.label, points: rewardPoints, color: primary.color, fill: true }], + xFormat: (v) => `${fmtInt(v)}步`, + emptyText: "等待首个 rollout 的 ep_rew_mean……", + }); + setText("chart-title", primary.label); + setText("chart-sub", describeSeries(rewardPoints, primary)); + + const metric = catalog.find((m) => m.key === state.chartKey) || primary; + const metricPoints = slice(seriesOf(metric.key), windowSize); + const refs = []; + if (metric.key === "approx_kl") refs.push({ value: 0.05, color: "#f87171", label: "过猛阈值 0.05" }); + if (metric.key === "explained_variance") refs.push({ value: 0, color: "#93a1bd", label: "0（不如均值预测）" }); + const layout2 = window.Charts.line($("chart-metric"), { + series: [{ name: metric.label, points: metricPoints, color: metric.color, fill: true }], + refs: refs, + xFormat: (v) => `${fmtInt(v)}步`, + emptyText: `等待 ${metric.key} 数据……`, + }); + setText("chart2-title", metric.label); + setText("chart2-sub", describeSeries(metricPoints, metric)); + state.layouts = { reward: layout, metric: layout2, metricKey: metric.key, primary: primary }; +} + +function slice(points, size) { + if (!size || points.length <= size) return points; + return points.slice(points.length - size); +} + +function describeSeries(points, metric) { + if (!points.length) return "（无数据）"; + if (points.length === 1) return `仅 1 点 · 最新 ${fmtValue(metric, points[0][1])}`; + const first = points[0], last = points[points.length - 1]; + const delta = last[1] - first[1]; + const sign = delta > 0 ? "+" : ""; + return `${points.length} 点 · 窗口内 ${sign}${fmtValue(metric, delta)} · ` + + `最新 ${fmtValue(metric, last[1])} @ ${fmtInt(last[0])} 步`; +} + +function bindChartInteractions() { + for (const id of ["chart-reward", "chart-metric"]) { + const canvas = $(id); + if (!canvas) continue; + const overlay = document.createElement("canvas"); + overlay.className = "chart-overlay"; + overlay.style.width = "100%"; overlay.style.height = "100%"; + if (canvas.parentElement) { + canvas.parentElement.style.position = "relative"; + canvas.parentElement.appendChild(overlay); + } + const move = (event) => { + const show = $("chart-cursor").checked; + const rect = canvas.getBoundingClientRect(); + const which = id === "chart-reward" ? "reward" : "metric"; + const layout = state.layouts && state.layouts[which]; + const ctx = overlay.getContext("2d"); + const dpr = window.devicePixelRatio || 1; + if (overlay.width !== Math.round(rect.width * dpr) || overlay.height !== Math.round(rect.height * dpr)) { + overlay.width = Math.round(rect.width * dpr); + overlay.height = Math.round(rect.height * dpr); + } + ctx.setTransform(dpr, 0, 0, dpr, 0, 0); + ctx.clearRect(0, 0, rect.width, rect.height); + if (!layout || !show || !isFinite(layout.bounds.x0)) return; + window.Charts.drawCursor(overlay, layout, { + mouse: { x: event.clientX - rect.left, y: event.clientY - rect.top }, + xFormat: (v) => `${fmtInt(v)}步`, + }); + }; + canvas.addEventListener("mousemove", move); + canvas.addEventListener("mouseleave", () => { + const ctx = overlay.getContext("2d"); + ctx.clearRect(0, 0, overlay.width, overlay.height); + }); + } + $("chart-window").addEventListener("change", renderCharts); + $("chart-cursor").addEventListener("change", renderCharts); +} + +// ---------------------------------------------------------------- 日志 +function renderLog(job) { + const lines = job.log_lines || []; + const filter = ($("log-filter").value || "").trim().toLowerCase(); + const showJson = $("log-json").checked; + const errorsOnly = $("log-errors-only").checked; + const view = $("log-view"); + const signature = `${lines.length}|${job.log_total}|${filter}|${showJson}|${errorsOnly}|${lines[lines.length - 1] || ""}`; + if (signature === state.lastLogSignature) return; + state.lastLogSignature = signature; + + const follow = $("log-follow").checked; + const atBottom = view.scrollTop + view.clientHeight >= view.scrollHeight - 24; + const keep = follow || atBottom; + + const html = []; + let shown = 0; + for (let i = 0; i < lines.length; i++) { + const raw = lines[i]; + const lower = raw.toLowerCase(); + if (filter && !lower.includes(filter)) continue; + if (errorsOnly && !/error|traceback|failed|exception|错误|失败/.test(lower)) continue; + if (!showJson && raw.trim().startsWith("{") && raw.includes('"event"')) continue; + shown++; + const cls = classify(raw); + html.push(`${highlight(raw, filter)}`); + } + view.innerHTML = html.length ? html.join("") : '（无匹配日志行）'; + if (keep) view.scrollTop = view.scrollHeight; + setText("log-shown", `显示 ${shown} / 共 ${fmtInt(job.log_total)} 行`); + setText("log-meta", `stdout/stderr 合并捕获 · 服务端保留最近 ${fmtInt(job.process ? job.process.log_capacity : 4000)} 行`); +} + +function classify(line) { + const text = line.toLowerCase(); + if (/error|traceback|exception|failed|错误|失败/.test(text)) return "err"; + if (/warn|deprecat|警告/.test(text)) return "warn"; + if (line.trim().startsWith("{")) return "json"; + if (/^\s*[-=]{3,}|已保存|success|complete|评估|metadata/.test(text)) return "meta"; + if (!line.trim()) return "dim"; + return ""; +} + +function highlight(line, filter) { + const escaped = escapeHtml(line); + if (!filter) return escaped; + try { + return escaped.replace(new RegExp(`(${filter.replace(/[.*+?^${}()|[\]\\]/g, "\\$&")})`, "gi"), + "$1"); + } catch (err) { return escaped; } +} + +// ---------------------------------------------------------------- 逐回合 +function renderEpisodes(job) { + const rows = job.monitor || []; + const tbody = $("episode-table").querySelector("tbody"); + const tail = rows.slice(-200).reverse(); + tbody.innerHTML = tail.map((row, index) => { + const seq = rows.length - index; + return `${seq}${fmtNumber(row.r)}` + + `${fmtNumber(row.l, 0)}${fmtNumber(row.t, 3)}`; + }).join("") || '尚无逐回合记录（Monitor CSV 随训练写入）'; +} + +// ---------------------------------------------------------------- 交互 +$("config-form").addEventListener("submit", async (event) => { + event.preventDefault(); + if (state.saving) return; + const err = $("form-error"); + err.textContent = ""; + const values = collectValues(); + const problems = validateLocally(values); + if (problems.length) { err.textContent = problems[0]; return; } + const payload = {}; + for (const [key, value] of Object.entries(values)) { + if (value !== null) payload[key] = value; + } + if (payload.resume && !payload.model) { err.textContent = "续训需要填写模型输出路径"; return; } + const res = await fetch("/api/train/start", { + method: "POST", headers: { "Content-Type": "application/json" }, + body: JSON.stringify(payload), + }); + const body = await res.json().catch(() => ({})); + if (!res.ok) { err.textContent = body.error || `启动失败（HTTP ${res.status}）`; return; } + state.preview = false; + state.jobKey = null; + state.metrics = []; + state.metricCache = []; + state.metricNext = 0; + state.metricTotal = 0; + await poll(); +}); + +$("btn-stop").addEventListener("click", async () => { + if (!confirm("确认停止当前训练？\n将向独立进程组发送 SIGTERM，本轮模型可能未保存。")) return; + const res = await fetch("/api/train/stop", { + method: "POST", headers: { "Content-Type": "application/json" }, body: "{}", + }); + const body = await res.json().catch(() => ({})); + if (!res.ok) showBanner("bad", `停止失败：${escapeHtml(body.error || res.status)}`); + await poll(); +}); + +$("btn-refresh").addEventListener("click", async () => { + state.preview = false; + showBanner("", ""); + await poll(); +}); + +$("btn-preview").addEventListener("click", async () => { + try { + const scenario = $("preview-scenario") ? $("preview-scenario").value : "rising"; + const res = await fetch(`/api/train/state?preview=1&scenario=${encodeURIComponent(scenario)}&log_tail=400`); + const payload = await res.json(); + if (!payload.job) throw new Error(payload.error || "服务端未返回示例快照"); + state.preview = true; + state.jobKey = null; + state.metrics = []; + state.metricCache = []; + state.metricNext = 0; + state.metricTotal = 0; + render(payload.job); + } catch (err) { + showBanner("bad", `离线预览不可用：${escapeHtml(err.message)}`); + } +}); + +$("btn-reset").addEventListener("click", () => { + applyValues({}); + $("form-error").textContent = ""; +}); + +for (const id of ["log-filter", "log-json", "log-errors-only", "log-tail"]) { + $(id).addEventListener("input", () => { + state.lastLogSignature = ""; + poll(); + }); +} + +boot(); diff --git a/src/mcm26b/training_ui/static/charts.js b/src/mcm26b/training_ui/static/charts.js new file mode 100644 index 0000000..e34a8ce --- /dev/null +++ b/src/mcm26b/training_ui/static/charts.js @@ -0,0 +1,255 @@ +/* 训练前端图表：原生 canvas 折线图，无外部依赖（离线可用，与评测前端同风格）。 + 只做三件事：坐标轴自适应、多序列折线、悬停读数；不引入缩放/图例等交互， + 因为训练曲线每 2 秒整体重绘一次，重量级图表库在这里只会增加风险。 + + 对外接口（挂到 globalThis.Charts）： + Charts.fit(canvas) 按 DPR 调整画布像素 + Charts.line(canvas, {series, refs, ...}) 折线图 + Charts.sparkline(canvas, values, opts) 指标卡片内的小趋势线 + 数据约定：series = [{ points: [[x, y], ...], color, width, fill }] */ +(function (global) { + "use strict"; + + var AXIS = "#2a3550", GRID = "rgba(42,53,80,.55)", DIM = "#93a1bd", TEXT = "#e6ecf8"; + var FONT = '11px ui-monospace, SFMono-Regular, Menlo, monospace'; + + function fit(canvas) { + var dpr = global.devicePixelRatio || 1; + var rect = canvas.getBoundingClientRect ? canvas.getBoundingClientRect() : null; + var cssWidth = (rect && rect.width) || canvas.clientWidth || canvas.width || 640; + var cssHeight = (rect && rect.height) || canvas.clientHeight || canvas.height || 200; + var w = Math.max(160, Math.round(cssWidth * dpr)); + var h = Math.max(80, Math.round(cssHeight * dpr)); + if (canvas.width !== w || canvas.height !== h) { canvas.width = w; canvas.height = h; } + var ctx = canvas.getContext("2d"); + ctx.setTransform(dpr, 0, 0, dpr, 0, 0); + return { ctx: ctx, w: w / dpr, h: h / dpr }; + } + + /* 第 0 层：数值格式（科学计数法只用于真正小而大的量级） */ + function fmt(value) { + if (value === null || value === undefined || Number.isNaN(value)) return "—"; + var abs = Math.abs(value); + if (abs !== 0 && (abs < 1e-3 || abs >= 1e5)) return value.toExponential(2); + if (abs >= 100) return value.toFixed(1); + if (abs >= 1) return value.toFixed(2); + if (abs >= 0.01) return value.toFixed(3); + return value.toFixed(4); + } + + function niceTicks(min, max, count) { + if (!isFinite(min) || !isFinite(max)) return [0, 1]; + if (min === max) { var pad = Math.abs(min) > 1e-9 ? Math.abs(min) * 0.1 : 0.5; + min -= pad; max += pad; } + var span = max - min; + var raw = span / Math.max(1, count); + var mag = Math.pow(10, Math.floor(Math.log10(raw))); + var norm = raw / mag; + var step = (norm <= 1 ? 1 : norm <= 2 ? 2 : norm <= 5 ? 5 : 10) * mag; + var start = Math.ceil(min / step) * step; + var ticks = []; + for (var v = start; v <= max + step * 1e-6 && ticks.length < 40; v += step) { + ticks.push(Math.abs(v) < step * 1e-9 ? 0 : v); + } + if (!ticks.length) ticks = [min, max]; + return ticks; + } + + function bounds(series) { + var xs = [], ys = []; + for (var i = 0; i < series.length; i++) { + var pts = series[i].points || []; + for (var j = 0; j < pts.length; j++) { + var p = pts[j]; + if (!p || !isFinite(p[0]) || !isFinite(p[1])) continue; + xs.push(p[0]); ys.push(p[1]); + } + } + if (!xs.length) return null; + var x0 = Math.min.apply(null, xs), x1 = Math.max.apply(null, xs); + var y0 = Math.min.apply(null, ys), y1 = Math.max.apply(null, ys); + if (x0 === x1) { x0 -= 0.5; x1 += 0.5; } + if (y0 === y1) { var pad = Math.abs(y0) > 1e-9 ? Math.abs(y0) * 0.08 : 0.5; + y0 -= pad; y1 += pad; } + return { x0: x0, x1: x1, y0: y0, y1: y1 }; + } + + /* ------------------------------------------------------------ 折线图 */ + function line(canvas, options) { + var opts = options || {}; + var axes = fit(canvas), ctx = axes.ctx, W = axes.w, H = axes.h; + var padL = opts.padLeft || 62, padR = 14, padT = 10, padB = 24; + ctx.clearRect(0, 0, W, H); + + var series = (opts.series || []).filter(function (s) { + return s && s.points && s.points.length; + }); + var box = bounds(series); + if (!box) { + ctx.fillStyle = DIM; ctx.font = FONT; + ctx.fillText(opts.emptyText || "等待数据……", padL, H / 2); + return null; + } + var yTicks = niceTicks(box.y0, box.y1, 4); + var yMin = Math.min(box.y0, yTicks[0]), yMax = Math.max(box.y1, yTicks[yTicks.length - 1]); + var plotW = Math.max(10, W - padL - padR), plotH = Math.max(10, H - padT - padB); + var sx = function (x) { return padL + (x - box.x0) / (box.x1 - box.x0) * plotW; }; + var sy = function (y) { return padT + (1 - (y - yMin) / (yMax - yMin)) * plotH; }; + + // 网格与刻度 + ctx.font = FONT; ctx.lineWidth = 1; + for (var i = 0; i < yTicks.length; i++) { + var y = sy(yTicks[i]); + ctx.strokeStyle = GRID; ctx.beginPath(); + ctx.moveTo(padL, y + 0.5); ctx.lineTo(padL + plotW, y + 0.5); ctx.stroke(); + ctx.fillStyle = DIM; ctx.textAlign = "right"; + ctx.fillText(fmt(yTicks[i]), padL - 6, y + 3.5); + } + ctx.textAlign = "center"; + for (var t = 0; t <= 4; t++) { + var xv = box.x0 + (box.x1 - box.x0) * (t / 4); + var x = sx(xv); + ctx.fillStyle = DIM; + ctx.fillText(opts.xFormat ? opts.xFormat(xv) : fmt(xv), x, H - 7); + } + ctx.textAlign = "left"; + ctx.strokeStyle = AXIS; ctx.beginPath(); + ctx.moveTo(padL + 0.5, padT); ctx.lineTo(padL + 0.5, padT + plotH); + ctx.lineTo(padL + plotW, padT + plotH); ctx.stroke(); + + // 参考线（例如 KL 的 0.05 阈值、解释方差的 0） + var refs = opts.refs || []; + for (var r = 0; r < refs.length; r++) { + var ref = refs[r]; + if (ref.value === undefined || ref.value < yMin || ref.value > yMax) continue; + var ry = sy(ref.value); + ctx.save(); + ctx.setLineDash([4, 4]); ctx.strokeStyle = ref.color || "#f59e0b"; + ctx.beginPath(); ctx.moveTo(padL, ry); ctx.lineTo(padL + plotW, ry); ctx.stroke(); + ctx.restore(); + if (ref.label) { + ctx.fillStyle = ref.color || "#f59e0b"; ctx.font = FONT; + ctx.fillText(ref.label, padL + 6, ry - 4); + } + } + + // 序列 + for (var s = 0; s < series.length; s++) { + var item = series[s]; + var pts = item.points; + ctx.strokeStyle = item.color || "#4f8cff"; + ctx.lineWidth = item.width || 1.6; + ctx.lineJoin = "round"; + ctx.beginPath(); + for (var k = 0; k < pts.length; k++) { + var px = sx(pts[k][0]), py = sy(pts[k][1]); + if (k === 0) ctx.moveTo(px, py); else ctx.lineTo(px, py); + } + ctx.stroke(); + if (item.fill && pts.length > 1) { + ctx.save(); + ctx.globalAlpha = 0.12; ctx.fillStyle = item.color || "#4f8cff"; + ctx.lineTo(sx(pts[pts.length - 1][0]), padT + plotH); + ctx.lineTo(sx(pts[0][0]), padT + plotH); + ctx.closePath(); ctx.fill(); ctx.restore(); + } + // 末点高亮：一眼看到最新值 + var last = pts[pts.length - 1]; + ctx.fillStyle = item.color || "#4f8cff"; + ctx.beginPath(); ctx.arc(sx(last[0]), sy(last[1]), 2.6, 0, Math.PI * 2); ctx.fill(); + } + + return { bounds: box, sx: sx, sy: sy, padL: padL, padT: padT, + plotW: plotW, plotH: plotH, yMin: yMin, yMax: yMax, series: series }; + } + + /* 悬停读数：把鼠标 x 映射到最接近的数据点，返回一次绘制的覆盖层信息 */ + function drawCursor(canvas, layout, options) { + var opts = options || {}; + if (!layout || !layout.series || !layout.series.length) return; + var axes = fit(canvas), ctx = axes.ctx; + var mouse = opts.mouse; + if (!mouse || mouse.x < layout.padL || mouse.x > layout.padL + layout.plotW) return; + var target = layout.bounds.x0 + + (mouse.x - layout.padL) / layout.plotW * (layout.bounds.x1 - layout.bounds.x0); + var lines = [], snapX = target; + for (var s = 0; s < layout.series.length; s++) { + var pts = layout.series[s].points; + if (!pts.length) continue; + // 数据已按 x 递增，二分定位最近点 + var lo = 0, hi = pts.length - 1, best = pts[hi]; + while (lo <= hi) { + var mid = (lo + hi) >> 1; + if (pts[mid][0] === target) { best = pts[mid]; break; } + if (pts[mid][0] < target) { best = pts[mid]; lo = mid + 1; } + else { hi = mid - 1; } + } + var next = pts[Math.min(pts.length - 1, lo)]; + if (next && Math.abs(next[0] - target) < Math.abs(best[0] - target)) best = next; + snapX = best[0]; + lines.push({ label: layout.series[s].name || "", color: layout.series[s].color, + value: best[1] }); + } + var cx = layout.sx(snapX); + ctx.save(); + ctx.setLineDash([3, 3]); ctx.strokeStyle = "rgba(230,236,248,.35)"; + ctx.beginPath(); ctx.moveTo(cx, layout.padT); + ctx.lineTo(cx, layout.padT + layout.plotH); ctx.stroke(); + ctx.restore(); + for (var i = 0; i < lines.length; i++) { + var item = lines[i]; + var cy = layout.sy(item.value); + ctx.fillStyle = item.color || "#4f8cff"; + ctx.beginPath(); ctx.arc(cx, cy, 3.2, 0, Math.PI * 2); ctx.fill(); + ctx.strokeStyle = "rgba(11,15,26,.9)"; ctx.lineWidth = 1.4; ctx.stroke(); + } + var text = (opts.xFormat ? opts.xFormat(snapX) : fmt(snapX)) + " · " + + lines.map(function (item) { return (item.label ? item.label + " " : "") + fmt(item.value); }) + .join(" "); + ctx.font = FONT; + var tw = ctx.measureText(text).width + 14; + var bx = Math.min(cx + 8, layout.padL + layout.plotW - tw); + bx = Math.max(layout.padL + 2, bx); + var by = layout.padT + 4; + ctx.fillStyle = "rgba(23,30,46,.94)"; ctx.strokeStyle = AXIS; + ctx.beginPath(); + if (ctx.roundRect) { ctx.roundRect(bx, by, tw, 20, 6); ctx.fill(); ctx.stroke(); } + else { ctx.fillRect(bx, by, tw, 20); ctx.strokeRect(bx, by, tw, 20); } + ctx.fillStyle = TEXT; ctx.textAlign = "left"; + ctx.fillText(text, bx + 7, by + 14); + } + + /* ------------------------------------------------------------ 迷你趋势线 */ + function sparkline(canvas, values, options) { + var opts = options || {}; + var axes = fit(canvas), ctx = axes.ctx, W = axes.w, H = axes.h; + ctx.clearRect(0, 0, W, H); + var clean = (values || []).filter(function (v) { return isFinite(v); }); + if (clean.length < 2) { + ctx.strokeStyle = GRID; ctx.beginPath(); + ctx.moveTo(0, H - 1.5); ctx.lineTo(W, H - 1.5); ctx.stroke(); + return; + } + var lo = Math.min.apply(null, clean), hi = Math.max.apply(null, clean); + if (lo === hi) { lo -= 0.5; hi += 0.5; } + var pad = 2; + var X = function (i) { return (i / (clean.length - 1)) * W; }; + var Y = function (v) { return H - pad - (v - lo) / (hi - lo) * (H - pad * 2); }; + ctx.strokeStyle = opts.color || "#4f8cff"; ctx.lineWidth = 1.4; + ctx.beginPath(); + for (var i = 0; i < clean.length; i++) { + if (i === 0) ctx.moveTo(X(i), Y(clean[i])); else ctx.lineTo(X(i), Y(clean[i])); + } + ctx.stroke(); + if (opts.fill !== false) { + ctx.save(); ctx.globalAlpha = 0.14; ctx.fillStyle = opts.color || "#4f8cff"; + ctx.lineTo(W, H); ctx.lineTo(0, H); ctx.closePath(); ctx.fill(); ctx.restore(); + } + var lastX = X(clean.length - 1), lastY = Y(clean[clean.length - 1]); + ctx.fillStyle = opts.color || "#4f8cff"; + ctx.beginPath(); ctx.arc(lastX - 1.5, lastY, 2.2, 0, Math.PI * 2); ctx.fill(); + } + + global.Charts = { fit: fit, line: line, drawCursor: drawCursor, + sparkline: sparkline, fmt: fmt }; +})(typeof globalThis !== "undefined" ? globalThis : this); diff --git a/src/mcm26b/training_ui/static/index.html b/src/mcm26b/training_ui/static/index.html new file mode 100644 index 0000000..ff6bd4a --- /dev/null +++ b/src/mcm26b/training_ui/static/index.html @@ -0,0 +1,174 @@ + + + + + +模型训练前端 · CUMCM 2026 B HRL + + + +
    +
    +

    模型训练前端 HRL 高层 MaskablePPO

    +

    尚未启动训练任务

    +
    +
    + 进程未启动 + 进度— + 墙钟— + 设备— + 连接探测中 +
    +
    + + + + +
    +
    + +
    + + + +
    +
    +

    执行进度与进程

    + 启动任务后显示子进程与步进进度 +
    + +
    +
    +
    + 0 / 0 环境步 + 0.0% +
    +
    +
    + 剩余估计 — + 预计结束 — + 进度点 0 +
    +
    +
    +
    子进程 PID—
    +
    进程组 / 会话独立
    +
    吞吐 FPS—
    +
    已用墙钟—
    +
    退出码—
    +
    日志行数0
    +
    +
    + +
    +

    +
    + + +
    +
    +

    训练配置

    + 字段与后端 TrainConfig 同源；留空字段取默认值 +
    +
    +
    +
    +
    + + + +
    +
    +
    + + +
    +
    +

    参数收敛状态

    + 判据为最近窗口的斜率与波动，仅供人工复核 +
    +
    + 判读等待数据 +
      +
      +
      + +
      +
      + + +
      +
      +
      +
      平均回合奖励
      + +
      +
      +
      近似 KL
      + +
      +
      +
      + + +
      +
      +

      实时运行日志

      + stdout/stderr 合并捕获 +
      +
      + + + + + + +
      + 
      （暂无输出）
      +
      + + +
      +
      +

      逐回合记录

      + 来自训练脚本写入的 Monitor CSV（每回合奖励/长度/时刻） +
      +
      + + + +
      #奖励 r长度 l时刻 t (s)
      +
      +
      +
      + +
      + 本地回环训练前端：与评测前端共用「白名单静态资源 + JSON 轮询 API」模式； + 命令由服务端以参数列表构造（不经过 shell），停止时向独立进程组发送 SIGTERM。 +
      + + + + + diff --git a/src/mcm26b/training_ui/static/style.css b/src/mcm26b/training_ui/static/style.css new file mode 100644 index 0000000..0859caf --- /dev/null +++ b/src/mcm26b/training_ui/static/style.css @@ -0,0 +1,221 @@ +/* 训练前端样式：沿用评测前端（referee/static/style.css）的暗色变量与圆角面板 + 语言，保证两个界面像同一套系统的两个页面；本页信息密度更高： + 进度条、进程时间线、参数卡片网格、曲线与日志终端。 */ + +:root { + --bg: #0f1420; --panel: #171e2e; --panel2: #1d2740; --line: #2a3550; + --text: #e6ecf8; --dim: #93a1bd; --accent: #4f8cff; --ok: #34d399; + --warn: #f59e0b; --bad: #f87171; --cyan: #38bdf8; --gray: #6b7280; + --mono: ui-monospace, SFMono-Regular, Menlo, Consolas, monospace; +} +* { box-sizing: border-box; } +body { + margin: 0; background: var(--bg); color: var(--text); + font: 14px/1.5 "PingFang SC", "Microsoft YaHei", system-ui, sans-serif; +} +code, .mono { background: var(--panel2); padding: 1px 6px; border-radius: 4px; color: var(--cyan); font-family: var(--mono); font-size: 12px; } +main { max-width: 1240px; margin: 0 auto; padding: 16px 22px 48px; } +footer { color: var(--dim); font-size: 12px; text-align: center; padding: 14px 22px; border-top: 1px solid var(--line); } +.hidden { display: none !important; } +.hint { color: var(--dim); font-size: 12px; } +h2 { font-size: 15px; margin: 0; } + +/* ------------------------------------------------------------ 顶栏 */ +.topbar { + position: sticky; top: 0; z-index: 20; + display: flex; align-items: center; gap: 18px; flex-wrap: wrap; + padding: 12px 22px; border-bottom: 1px solid var(--line); + background: rgba(23,30,46,.96); backdrop-filter: blur(6px); +} +.brand h1 { font-size: 17px; margin: 0; } +.brand .sub { font-size: 12px; color: var(--dim); font-weight: normal; margin-left: 8px; } +.brand .tagline { margin: 3px 0 0; color: var(--dim); font-size: 12px; font-family: var(--mono); } +.topstatus { display: flex; gap: 8px; flex-wrap: wrap; margin-left: auto; } +.topactions { display: flex; gap: 8px; } + +.chip { + background: var(--panel2); border: 1px solid var(--line); border-radius: 999px; + padding: 3px 12px; font-size: 12px; color: var(--dim); white-space: nowrap; +} +.chip b { color: var(--text); font-weight: 600; margin-left: 4px; } +.chip.gray b { color: var(--gray); } +.chip.ok b { color: var(--ok); } +.chip.warn b { color: var(--warn); } +.chip.bad b { color: var(--bad); } +.chip.cyan b { color: var(--cyan); } + +button, .button { + background: var(--panel2); color: var(--text); border: 1px solid var(--line); + border-radius: 6px; padding: 7px 14px; font-size: 13px; cursor: pointer; +} +button:hover:enabled, .button:hover { border-color: var(--accent); } +button:disabled { opacity: .45; cursor: not-allowed; } +button.primary { background: var(--accent); border-color: var(--accent); color: #fff; } +button.primary:hover:enabled { filter: brightness(1.08); } +button.danger { background: transparent; border-color: var(--bad); color: var(--bad); } +button.danger:hover:enabled { background: rgba(248,113,113,.12); } + +.banner { + border: 1px solid var(--bad); border-left: 4px solid var(--bad); + background: rgba(248,113,113,.08); border-radius: 8px; + padding: 10px 14px; margin: 14px 0 0; font-size: 13px; +} +.banner.ok { border-color: var(--ok); border-left-color: var(--ok); background: rgba(52,211,153,.08); } +.banner.warn { border-color: var(--warn); border-left-color: var(--warn); background: rgba(245,158,11,.08); } +.banner b { font-family: var(--mono); } + +/* ------------------------------------------------------------ 面板 */ +.panel { + border: 1px solid var(--line); border-radius: 12px; background: var(--panel); + padding: 14px 16px; margin-top: 16px; +} +.panel-head { display: flex; align-items: baseline; gap: 12px; margin-bottom: 12px; flex-wrap: wrap; } +.panel-head h2 { color: var(--text); } +.panel-head h2::before { + content: ""; display: inline-block; width: 3px; height: 13px; margin-right: 8px; + background: var(--accent); border-radius: 2px; vertical-align: -1px; +} + +/* ------------------------------------------------------------ 进度 */ +.progress-row { display: flex; gap: 20px; align-items: stretch; flex-wrap: wrap; } +.progress-main { flex: 1 1 380px; min-width: 320px; } +.progress-labels { display: flex; justify-content: space-between; font-size: 13px; margin-bottom: 6px; } +.progress-labels b { font-family: var(--mono); font-size: 15px; } +.bar { height: 12px; border-radius: 999px; background: #0b0f1a; border: 1px solid var(--line); overflow: hidden; } +.bar-fill { + height: 100%; width: 0%; border-radius: 999px; + background: linear-gradient(90deg, #2f6fe0, var(--accent) 60%, var(--cyan)); + transition: width .3s ease; +} +.bar-fill.warn { background: linear-gradient(90deg, #b45309, var(--warn)); } +.bar-fill.bad { background: linear-gradient(90deg, #b91c1c, var(--bad)); } +.bar-fill.ok { background: linear-gradient(90deg, #059669, var(--ok)); } +.progress-foot { display: flex; gap: 18px; margin-top: 7px; flex-wrap: wrap; } + +.stat-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(132px, 1fr)); gap: 8px; flex: 1 1 420px; } +.stat { + border: 1px solid var(--line); border-radius: 8px; background: var(--panel2); + padding: 7px 10px; display: flex; flex-direction: column; gap: 2px; +} +.stat .k { color: var(--dim); font-size: 11px; } +.stat .v { font-family: var(--mono); font-size: 15px; } +.stat .v.ok { color: var(--ok); } +.stat .v.warn { color: var(--warn); } +.stat .v.bad { color: var(--bad); } + +.timeline { display: flex; align-items: center; gap: 0; margin-top: 16px; flex-wrap: wrap; } +.node { display: flex; align-items: center; gap: 8px; } +.node .dot { + width: 12px; height: 12px; border-radius: 50%; border: 2px solid var(--line); + background: var(--panel2); flex: none; +} +.node .txt { font-size: 12px; color: var(--dim); } +.node.done .dot { border-color: var(--ok); background: var(--ok); } +.node.done .txt { color: var(--text); } +.node.active .dot { border-color: var(--accent); background: var(--accent); box-shadow: 0 0 0 4px rgba(79,140,255,.18); } +.node.active .txt { color: var(--accent); font-weight: 600; } +.node.failed .dot { border-color: var(--bad); background: var(--bad); } +.node.failed .txt { color: var(--bad); } +.link { width: 34px; height: 2px; background: var(--line); flex: none; margin: 0 8px; } +.link.done { background: var(--ok); } +#command-preview { margin: 12px 0 0; font-family: var(--mono); word-break: break-all; line-height: 1.7; } + +/* ------------------------------------------------------------ 表单 */ +.presets { display: flex; flex-wrap: wrap; gap: 8px; margin-bottom: 14px; } +.preset { + border: 1px solid var(--line); border-radius: 8px; background: var(--panel2); + padding: 7px 12px; font-size: 12px; cursor: pointer; color: var(--text); +} +.preset:hover { border-color: var(--accent); } +.preset .note { display: block; color: var(--dim); font-size: 11px; margin-top: 2px; } +.form-groups { display: grid; grid-template-columns: repeat(auto-fit, minmax(268px, 1fr)); gap: 14px; } +fieldset.group { border: 1px solid var(--line); border-radius: 10px; padding: 10px 12px 12px; margin: 0; background: #141b29; } +fieldset.group legend { color: var(--dim); font-size: 12px; padding: 0 6px; } +fieldset.group .ghint { color: var(--dim); font-size: 11px; margin: 0 0 8px; } +.field { display: flex; flex-direction: column; gap: 4px; margin-bottom: 10px; } +.field .flabel { font-size: 12px; color: var(--dim); display: flex; gap: 6px; align-items: baseline; } +.field .flabel .unit { color: var(--gray); font-size: 11px; } +.field input[type="text"], .field input[type="number"], .field select { + background: var(--panel2); border: 1px solid var(--line); color: var(--text); + border-radius: 6px; padding: 6px 9px; font-size: 13px; width: 100%; + font-family: var(--mono); +} +.field input:focus, .field select:focus, .log-toolbar input:focus { outline: none; border-color: var(--accent); } +.field .fhelp { font-size: 11px; color: var(--gray); } +.field.check { flex-direction: row; align-items: center; gap: 8px; } +.field.check input { width: auto; } +.field.dirty input, .field.dirty select { border-color: rgba(79,140,255,.65); } +.field.invalid input, .field.invalid select { border-color: var(--bad); } +.form-foot { display: flex; align-items: center; gap: 12px; margin-top: 14px; flex-wrap: wrap; } +.form-error { color: var(--bad); font-size: 12px; } + +/* ------------------------------------------------------------ 收敛 */ +.converge-head { display: flex; gap: 14px; align-items: flex-start; flex-wrap: wrap; margin-bottom: 12px; } +.notes { margin: 0; padding-left: 18px; color: var(--dim); font-size: 12px; max-width: 760px; } +.notes li { margin: 2px 0; } +.metric-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(186px, 1fr)); gap: 10px; } +.metric-card { + border: 1px solid var(--line); border-radius: 10px; background: var(--panel2); + padding: 9px 11px; display: flex; flex-direction: column; gap: 5px; +} +.metric-card .mhead { display: flex; justify-content: space-between; align-items: baseline; gap: 8px; } +.metric-card .mname { font-size: 12px; color: var(--dim); } +.metric-card .mvalue { font-family: var(--mono); font-size: 17px; } +.metric-card .mfoot { display: flex; justify-content: space-between; font-size: 11px; color: var(--gray); gap: 6px; } +.metric-card canvas { width: 100%; height: 40px; display: block; } +.trend.up { color: var(--ok); } .trend.down { color: var(--warn); } +.trend.flat { color: var(--dim); } .trend.none { color: var(--gray); } +.metric-card.alert { border-color: rgba(248,113,113,.55); } +.metric-card.alert .mvalue { color: var(--bad); } + +/* ------------------------------------------------------------ 图表 */ +.chart-toolbar { display: flex; align-items: center; gap: 14px; flex-wrap: wrap; margin: 16px 0 8px; } +.chart-tabs { display: flex; gap: 6px; flex-wrap: wrap; } +.chart-tab { + border: 1px solid var(--line); background: var(--transparent, transparent); + color: var(--dim); border-radius: 999px; padding: 4px 12px; font-size: 12px; +} +.chart-tab.active { border-color: var(--accent); color: var(--text); background: var(--panel2); } +label.inline { font-size: 12px; color: var(--dim); display: flex; gap: 6px; align-items: center; } +label.inline input[type="checkbox"] { width: auto; } +label.inline select, .log-toolbar select { + background: var(--panel2); color: var(--text); border: 1px solid var(--line); + border-radius: 6px; padding: 4px 8px; font-size: 12px; +} +.chart-stack { display: flex; flex-direction: column; gap: 14px; } +.chart-figure { margin: 0; border: 1px solid var(--line); border-radius: 10px; background: #0b0f1a; padding: 10px 12px 4px; } +.chart-figure figcaption { display: flex; gap: 10px; align-items: baseline; font-size: 12px; margin-bottom: 6px; } +.chart-figure figcaption b { color: var(--text); font-size: 13px; } +.chart-figure canvas { width: 100%; display: block; } + +/* ------------------------------------------------------------ 日志 */ +.log-toolbar { display: flex; align-items: center; gap: 12px; flex-wrap: wrap; margin-bottom: 8px; } +.log-toolbar input[type="search"] { + background: var(--panel2); border: 1px solid var(--line); color: var(--text); + border-radius: 6px; padding: 6px 10px; font-size: 12px; min-width: 240px; +} +.log-view { + margin: 0; background: #0b0f1a; border: 1px solid var(--line); border-radius: 10px; + padding: 10px 12px; height: 320px; overflow: auto; font-family: var(--mono); + font-size: 12px; line-height: 1.55; white-space: pre-wrap; word-break: break-word; +} +.log-view .l { display: block; } +.log-view .l.err { color: var(--bad); } +.log-view .l.warn { color: var(--warn); } +.log-view .l.json { color: var(--cyan); } +.log-view .l.meta { color: var(--ok); } +.log-view .l.dim { color: var(--gray); } +.log-view mark { background: rgba(245,158,11,.28); color: inherit; border-radius: 2px; } + +/* ------------------------------------------------------------ 表格 */ +.table-wrap { border: 1px solid var(--line); border-radius: 10px; overflow: auto; max-height: 300px; } +table { width: 100%; border-collapse: collapse; font-size: 12px; } +th, td { text-align: left; padding: 6px 10px; border-bottom: 1px solid var(--line); white-space: nowrap; } +th { position: sticky; top: 0; background: var(--panel2); color: var(--dim); font-weight: 500; } +td { font-family: var(--mono); } +tbody tr:hover { background: rgba(79,140,255,.06); } + +@media (max-width: 760px) { + .topstatus { margin-left: 0; } + .progress-row { gap: 12px; } +} diff --git a/tests/js/training_ui_harness.cjs b/tests/js/training_ui_harness.cjs new file mode 100644 index 0000000..0b773e0 --- /dev/null +++ b/tests/js/training_ui_harness.cjs @@ -0,0 +1,285 @@ +/* 训练前端 JS 逻辑回归（由 pytest 经 node 调起，见 tests/test_training_ui_js.py）。 + 以 DOM 桩加载发布的 charts.js / app.js 原文，用服务端真实生成的示例快照 + （services/demo.py 的 demo_snapshot JSON）驱动渲染，断言： + 1) 表单由 schema 生成、字段数与后端契约一致； + 2) 指标卡/曲线/日志/逐回合表都拿到数据并渲染，无异常抛出； + 3) 三种收敛场景的判读文案正确落到顶部 chip； + 4) 增量指标游标合并不丢点也不重点（曾是该前端的典型 bug）； + 5) charts.js 的刻度与格式函数在极端输入下不抛异常。 + 曾回归出两个"加载即白屏"隐患：job.process 缺失时直接取下标、以及游标 + 与 metric_limit 同时生效时重复追加同批进度点。本文件防其复发。 */ +"use strict"; +const fs = require("fs"); +const path = require("path"); + +const REPO = process.env.REPO; +if (!REPO) throw new Error("REPO env required"); +const STATIC = path.join(REPO, "src", "mcm26b", "training_ui", "static"); +const DEMO = process.env.DEMO_STATE; +if (!DEMO) throw new Error("DEMO_STATE env required"); + +const scenarios = JSON.parse(fs.readFileSync(DEMO, "utf8")); +const schema = JSON.parse(fs.readFileSync(process.env.SCHEMA, "utf8")); + +// ---------------------------------------------------------------- DOM 桩 +/* className 是唯一真源：代码里会直接赋值 className（见 chip()），也会用 + classList.add/toggle；如果 classList 自带一个独立 Set，两者就会不同步， + 测试会误报。这里让 classList 每次读写都落回 className。 */ +function makeClassList(el) { + const read = () => String(el.className || "").split(/\s+/).filter(Boolean); + const write = (list) => { el.className = [...new Set(list)].join(" "); }; + return { + add: (...c) => write(read().concat(c)), + remove: (...c) => write(read().filter((x) => !c.includes(x))), + toggle: (c, force) => { + const on = force === undefined ? !read().includes(c) : !!force; + write(on ? read().concat(c) : read().filter((x) => x !== c)); + return on; + }, + contains: (c) => read().includes(c), + }; +} +const ctxStub = new Proxy({}, { + get(t, prop) { + if (!(prop in t)) { + t[prop] = typeof prop === "string" && + /^(arc|beginPath|closePath|fill|stroke|moveTo|lineTo|fillText|strokeText|strokeRect|fillRect|clearRect|setLineDash|setTransform|save|restore|roundRect|measureText|scale|translate)$/.test(prop) + ? (prop === "measureText" ? () => ({ width: 40 }) : () => {}) + : function () {}; + } + return t[prop]; + }, + set(t, prop, v) { t[prop] = v; return true; }, +}); +const allElements = []; +/* querySelectorAll 桩：支持 app.js 实际用到的选择器 + [data-node="x"] / [data-link="n"] → 时间线节点与连接线 + .chart-tab → 曲线页签（用于切换 active） + tbody → 逐回合表体 + 子元素按 tag 记账，因此 page.querySelector("tbody") 每次返回同一个对象。 */ +function matchesSelector(el, sel) { + const attr = /^\[data-([a-z]+)="?([^"\]]*)"?\]$/.exec(sel); + if (attr) return el.dataset && String(el.dataset[attr[1]]) === attr[2]; + if (sel.startsWith(".")) return String(el.className || "").split(/\s+/).includes(sel.slice(1)); + if (el.tag === sel) return true; + const byTag = el.byTag && el.byTag[sel]; + return Boolean(byTag); +} +function makeEl(id, tag) { + const el = { + id, tag: tag || id, textContent: "", innerHTML: "", value: "", checked: true, + disabled: false, className: "", + files: [], width: 600, height: 240, style: {}, handlers: {}, children: [], + byTag: {}, dataset: {}, scrollTop: 0, scrollHeight: 100, clientHeight: 100, + get classList() { return makeClassList(this); }, + addEventListener(type, fn) { (this.handlers[type] ||= []).push(fn); }, + removeEventListener() {}, click() {}, matches() { return false; }, + appendChild(child) { this.children.push(child); return child; }, + removeChild(child) { this.children = this.children.filter((c) => c !== child); }, + setAttribute() {}, focus() {}, + querySelector(sel) { return this.querySelectorAll(sel)[0] || this.childFor(sel); }, + // 表单里的静态子元素（如 ）不在 children 里，按需惰性创建并缓存 + childFor(sel) { + if (!this.byTag[sel]) this.byTag[sel] = makeEl(`${id}:${sel}`, sel); + return this.byTag[sel]; + }, + querySelectorAll(sel) { + const found = []; + const walk = (node) => { + for (const child of node.children || []) { + if (matchesSelector(child, sel)) found.push(child); + walk(child); + } + }; + walk(this); + return found; + }, + getContext() { return ctxStub; }, + getBoundingClientRect() { return { left: 0, top: 0, width: 600, height: 240 }; }, + }; + allElements.push(el); + return el; +} +const els = new Map(); +const $get = (id) => { if (!els.has(id)) els.set(id, makeEl(id)); return els.get(id); }; +// 几个会被读取初值的控件（否则 value 为空字符串，Number("") === 0） +for (const [id, value] of [["log-tail", "400"], ["chart-window", "120"]]) $get(id).value = value; +$get("chart-cursor").checked = true; +$get("log-follow").checked = true; +$get("log-json").checked = true; +$get("log-errors-only").checked = false; // 只保留"显示进度 JSON"这一项过滤 +$get("preview-scenario").value = "rising"; + +globalThis.requestAnimationFrame = (cb) => { cb(0); return 1; }; +globalThis.cancelAnimationFrame = () => {}; +globalThis.performance = { now: () => Date.now() }; +globalThis.setInterval = () => 0; +globalThis.location = { port: "8036" }; +globalThis.confirm = () => true; +globalThis.alert = () => {}; +globalThis.addEventListener = () => {}; +globalThis.devicePixelRatio = 2; +globalThis.window = globalThis; +globalThis.document = { + getElementById: $get, + createElement: (tag) => makeEl(tag), + addEventListener() {}, +}; +// 元素挂载计数：断言"指标卡确实被创建"而不是静默空转 +let createdCount = 0; +const realCreate = globalThis.document.createElement; +globalThis.document.createElement = (tag) => { createdCount++; return realCreate(tag); }; + +globalThis.fetch = async (url) => { + const json = async () => { + if (url.startsWith("/api/train/schema")) return schema; + if (url.includes("state")) { + const match = /scenario=([a-z-]+)/.exec(url); + const key = match ? match[1] : "rising"; + return { job: scenarios[key] }; + } + throw new Error("unexpected fetch " + url); + }; + return { ok: true, status: 200, json }; +}; + +// ---------------------------------------------------------------- 装载发布代码 +const chartsSrc = fs.readFileSync(path.join(STATIC, "charts.js"), "utf8"); +const appSrc = fs.readFileSync(path.join(STATIC, "app.js"), "utf8"); +new Function(chartsSrc)(); +const appApi = new Function( + appSrc + "\n;return { seriesOf, classify, highlight, absorbMetrics, state, render, fmtNumber, fmtDuration };" +)(); +// app.js 末尾会自动 boot()，这里等它把 schema/首次快照渲染完 +const flush = async () => { for (let i = 0; i < 40; i++) await new Promise((r) => setImmediate(r)); }; +function assert(cond, message) { if (!cond) throw new Error(message); } +function stripTags(html) { return String(html).replace(/<[^>]*>/g, ""); } + +(async () => { + await flush(); + + // 1) schema 驱动表单 + const groups = $get("form-groups").children; + assert(groups.length === schema.groups.length, + `表单分组数应为 ${schema.groups.length}，实际 ${groups.length}`); + const cfgField = $get("cfg-timesteps"); + assert(cfgField && String(cfgField.value) === String(schema.fields.find((f) => f.key === "timesteps").default), + `默认值未写入表单: ${cfgField && cfgField.value}`); + assert($get("presets").children.length === schema.presets.length, "预设按钮数不匹配"); + + // 2) 首帧（rising 场景）渲染 + const state = appApi.state; + assert(state.metrics.length > 0, "首次快照后本地进度点为空"); + const chips = $get("chip-converge").innerHTML; + assert(chips.includes(scenarios.rising.convergence.status), + `收敛 chip 未渲染: ${chips}`); + assert($get("stat-pid").textContent === "PID 42424", `PID 未渲染: ${$get("stat-pid").textContent}`); + assert($get("metric-grid").children.length === schema.metrics.length, + `指标卡数量应为 ${schema.metrics.length}，实际 ${$get("metric-grid").children.length}`); + assert($get("chart-tabs").children.length === schema.metrics.length, + "曲线页签数量与指标字典不一致"); + assert(stripTags($get("log-view").innerHTML).includes("progress"), + "日志区没有渲染进度行"); + assert($get("episode-table").querySelector("tbody") !== null, "逐回合表缺少 tbody"); + const tbody = $get("episode-table").querySelector("tbody"); + assert(tbody.innerHTML.includes(""), + `逐回合表未渲染任何行: monitor=${(scenarios.rising.monitor || []).length}`); + assert(Number($get("steps-total").textContent.replace(/,/g, "")) === scenarios.rising.config.timesteps, + `总步数渲染异常: ${$get("steps-total").textContent}`); + + // 3) 三种收敛场景切到对应 chip + const tones = { rising: "ok", plateau: "ok", "kl-spike": "bad" }; + for (const key of Object.keys(scenarios)) { + const rendered = appApi.render(scenarios[key]); + void rendered; + const html = $get("chip-converge").innerHTML; + assert(html.includes(scenarios[key].convergence.status), + `场景 ${key} 的判读未落到 chip: ${html}`); + assert($get("chip-converge").classList.contains(tones[key]), + `场景 ${key} 的色调应为 ${tones[key]}`); + } + assert(String($get("chart2-title").textContent).length > 0, "第二张曲线标题为空"); + + // 4) 增量游标合并：续接 / 有缺口 / 过期批次 三条分支 + const job = JSON.parse(JSON.stringify(scenarios.rising)); + const total = job.metrics.length; + const reset = () => { + appApi.state.metrics = []; + appApi.state.metricCache = []; + appApi.state.metricNext = 0; + appApi.state.metricTotal = 0; + }; + const feed = (metrics, offset) => appApi.absorbMetrics( + { ...job, metrics, metric_offset: offset, metric_total: total }); + const stamp = (i) => job.metrics[i].timesteps; + + reset(); + feed(job.metrics.slice(0, 10), 0); + assert(appApi.state.metrics.length === 10, `首批应 10 点，实际 ${appApi.state.metrics.length}`); + feed(job.metrics.slice(10, 14), 10); + assert(appApi.state.metrics.length === 14, `续接后应 14 点，实际 ${appApi.state.metrics.length}`); + assert(appApi.state.metricNext === 14, `metricNext 应为 14，实际 ${appApi.state.metricNext}`); + feed([], 14); + assert(appApi.state.metrics.length === 14, `无新点不得改变长度，实际 ${appApi.state.metrics.length}`); + + // 有缺口（服务端窗口前移）：整体替换，避免曲线出现空洞 + feed(job.metrics.slice(20, 30), 20); + assert(appApi.state.metrics.length === 10, `有缺口时应替换为 10 点，实际 ${appApi.state.metrics.length}`); + assert(appApi.state.metrics[0].timesteps === stamp(20), "替换后起点错位"); + assert(appApi.state.metricNext === 30, `游标未随替换更新: ${appApi.state.metricNext}`); + + // 过期批次（服务端计数回退）：忽略，本地不动 + feed(job.metrics.slice(0, 5), 0); + assert(appApi.state.metrics.length === 10, `过期批次应被忽略，实际 ${appApi.state.metrics.length}`); + assert(appApi.state.metrics[0].timesteps === stamp(20), "过期批次污染了本地缓存"); + + // 字符串形式的 offset：必须按数值比较，不得退化成字典序 + reset(); + feed(job.metrics.slice(0, 10), "0"); + feed(job.metrics.slice(10, 14), "10"); + assert(appApi.state.metrics.length === 14, + `字符串 offset 应正常续接为 14 点，实际 ${appApi.state.metrics.length}`); + + // 缺失 offset（旧版服务端）：退化为按到达顺序追加，不得丢点 + reset(); + appApi.absorbMetrics({ ...job, metrics: job.metrics.slice(0, 6) }); + assert(appApi.state.metrics.length === 6, `缺 offset 时首批应 6 点，实际 ${appApi.state.metrics.length}`); + + // 5) 日志分类与高亮 + assert(appApi.classify("Traceback (most recent call last):") === "err", "Traceback 未归入错误行"); + assert(appApi.classify("UserWarning: x") === "warn", "警告未归入 warn"); + assert(appApi.classify('{"event": "progress"}') === "json", "进度 JSON 未归入 json"); + assert(appApi.highlight("abc", "b").includes("b"), "过滤高亮未生效"); + const marked = appApi.highlight("