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# 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）
README.md
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- [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
docs/hrl-p1-execution-report.md
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# 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`。
docs/hrl-sol-execution.md
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# 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/)。
docs/training-ui.md
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# 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。
logs/README.md
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# 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。
models/README.md
+4
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# 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。
output/hrl/manifests/error-smoke.json
+22
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@@ -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
}
]
}
]
}
output/hrl/manifests/p1-q3-3scene.json
+102
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@@ -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
}
]
}
]
}
output/hrl/manifests/p1-q4-3scene.json
+102
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@@ -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,
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"y": -53.50549468070716,
"r_eff_m": 1251.771750543368,
"directional": false,
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},
{
"channel": 18,
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"y": -131.35607931264857,
"r_eff_m": 1106.5738050310383,
"directional": false,
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}
]
},
{
"scene_id": "scene-92002",
"seed": 92002,
"error_seed": 102002,
"problem": 4,
"sources": [
{
"channel": 16,
"x": -797.6201997604195,
"y": -1471.5756774197087,
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output/hrl/manifests/smoke-hrl.json
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output/hrl/manifests/smoke-q3-bayes.json
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output/hrl/manifests/smoke-q3.json
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output/hrl/manifests/smoke-q4.json
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output/hrl/p1-bench-bayes/aggregate.csv
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output/hrl/p1-bench-bayes/aggregate.json
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output/hrl/p1-bench-bayes/manifest.json
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output/hrl/p1-bench-bayes/results.json
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output/hrl/p1-bench-bayes/runs/scene-91000/bayes/summary.json
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output/hrl/p1-bench-directional/aggregate.csv
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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
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2 scene-91000 directional 4 3 3 13456.997 4485.666 13456.997 1.038386 0.292 850 847 3 3 True all_resolved user_exit True True True [2, 4, 14] [] 0
output/hrl/p1-bench-directional/aggregate.json
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output/hrl/p1-bench-directional/manifest.json
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output/hrl/p1-bench-directional/results.json
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output/hrl/p1-bench-directional/runs/scene-91000/directional/session.jsonl
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output/hrl/p1-bench-directional/runs/scene-91000/directional/summary.json
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output/hrl/p1-bench-hrl/aggregate.csv
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output/hrl/p1-bench-hrl/aggregate.json
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output/hrl/p1-bench-hrl/manifest.json
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output/hrl/p1-bench-hrl/results.json
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output/hrl/p1-bench-hrl/runs/scene-91000/hrl/summary.json
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output/hrl/p1-bench-smoke/aggregate.csv
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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,hrl_heuristic,3,3,3,2613.443,871.148,2613.443,0.901554,0.063,137,134,3,3,True,all_resolved,user_exit,True,True,True,"[2, 4, 14]",[],0
1 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
2 scene-91000 hrl_heuristic 3 3 3 2613.443 871.148 2613.443 0.901554 0.063 137 134 3 3 True all_resolved user_exit True True True [2, 4, 14] [] 0
output/hrl/p1-bench-smoke/aggregate.json
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{
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output/hrl/p1-bench-smoke/manifest.json
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{
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output/hrl/p1-bench-smoke/results.json
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[
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output/hrl/p1-bench-smoke/runs/scene-91000/hrl_heuristic/session.jsonl
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output/hrl/p1-bench-smoke/runs/scene-91000/hrl_heuristic/summary.json
+2592
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output/hrl/p1-error-smoke/aggregate.csv
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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"
1 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
2 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
output/hrl/p1-error-smoke/aggregate.json
+27
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@@ -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
}
output/hrl/p1-error-smoke/manifest.json
+22
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@@ -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
}
]
}
]
}
output/hrl/p1-error-smoke/results.json
+30
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@@ -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,
"truth_channels": [
2
],
"declared_absent_truth": [],
"anomaly_count": 0,
"error": "RuntimeError: 未找到 HRL 模型 output/hrl/no-such-model.zip。请先训练：\n uv sync --group rl --group dev\n uv run python scripts/train_hrl_high.py --problem 3"
}
]
output/hrl/p1-multiround-q3/aggregate.csv
+10
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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,hrl,3,3,3,3560.92,1186.973,3560.92,3.357654,0.072,135,132,3,3,True,all_resolved,user_exit,True,True,True,"[2, 4, 14]",[],0
scene-91000,hrl_heuristic,3,3,3,2613.443,871.148,2613.443,0.864249,0.06,137,134,3,3,True,all_resolved,user_exit,True,True,True,"[2, 4, 14]",[],0
scene-91000,bayes,3,3,3,2613.443,871.148,2613.443,0.734953,0.057,137,134,3,3,True,all_resolved,user_exit,True,True,True,"[2, 4, 14]",[],0
scene-91001,hrl,3,3,3,3817.466,1272.489,3817.466,1.084374,0.07,135,132,3,3,True,all_resolved,user_exit,True,True,True,"[2, 3, 7]",[],0
scene-91001,hrl_heuristic,3,3,3,2432.42,810.807,2432.42,0.84206,0.06,139,136,3,3,True,all_resolved,user_exit,True,True,True,"[2, 3, 7]",[],0
scene-91001,bayes,3,3,3,2432.42,810.807,2432.42,0.725561,0.063,139,136,3,3,True,all_resolved,user_exit,True,True,True,"[2, 3, 7]",[],0
scene-91002,hrl,3,3,3,3815.755,1271.918,3815.755,1.117844,0.072,135,132,3,3,True,all_resolved,user_exit,True,True,True,"[3, 8, 11]",[],0
scene-91002,hrl_heuristic,3,3,3,2509.25,836.417,2509.25,0.943166,0.072,141,138,3,3,True,all_resolved,user_exit,True,True,True,"[3, 8, 11]",[],0
scene-91002,bayes,3,3,3,2509.25,836.417,2509.25,0.782048,0.069,141,138,3,3,True,all_resolved,user_exit,True,True,True,"[3, 8, 11]",[],0
1 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
2 scene-91000 hrl 3 3 3 3560.92 1186.973 3560.92 3.357654 0.072 135 132 3 3 True all_resolved user_exit True True True [2, 4, 14] [] 0
3 scene-91000 hrl_heuristic 3 3 3 2613.443 871.148 2613.443 0.864249 0.06 137 134 3 3 True all_resolved user_exit True True True [2, 4, 14] [] 0
4 scene-91000 bayes 3 3 3 2613.443 871.148 2613.443 0.734953 0.057 137 134 3 3 True all_resolved user_exit True True True [2, 4, 14] [] 0
5 scene-91001 hrl 3 3 3 3817.466 1272.489 3817.466 1.084374 0.07 135 132 3 3 True all_resolved user_exit True True True [2, 3, 7] [] 0
6 scene-91001 hrl_heuristic 3 3 3 2432.42 810.807 2432.42 0.84206 0.06 139 136 3 3 True all_resolved user_exit True True True [2, 3, 7] [] 0
7 scene-91001 bayes 3 3 3 2432.42 810.807 2432.42 0.725561 0.063 139 136 3 3 True all_resolved user_exit True True True [2, 3, 7] [] 0
8 scene-91002 hrl 3 3 3 3815.755 1271.918 3815.755 1.117844 0.072 135 132 3 3 True all_resolved user_exit True True True [3, 8, 11] [] 0
9 scene-91002 hrl_heuristic 3 3 3 2509.25 836.417 2509.25 0.943166 0.072 141 138 3 3 True all_resolved user_exit True True True [3, 8, 11] [] 0
10 scene-91002 bayes 3 3 3 2509.25 836.417 2509.25 0.782048 0.069 141 138 3 3 True all_resolved user_exit True True True [3, 8, 11] [] 0
output/hrl/p1-multiround-q3/aggregate.json
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{
"variants": {
"bayes": {
"episodes": 3,
"valid_episodes": 3,
"valid_rate": 1.0,
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output/hrl/p1-multiround-q3/runs/scene-91002/hrl_heuristic/summary.json
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output/hrl/p1-multiround-q4/aggregate.csv
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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-92000,hrl_heuristic,4,3,3,12779.703,4259.901,12779.703,6.558622,0.347,892,889,3,3,True,all_resolved,user_exit,True,True,True,"[1, 6, 16]",[],0
scene-92000,directional,4,3,3,13780.377,4593.459,13780.377,1.031098,0.252,886,883,3,3,True,all_resolved,user_exit,True,True,True,"[1, 6, 16]",[],0
scene-92001,hrl_heuristic,4,3,3,12059.045,4019.682,12059.045,6.361647,0.32,848,845,3,3,True,all_resolved,user_exit,True,True,True,"[7, 12, 18]",[],0
scene-92001,directional,4,3,3,13412.5,4470.833,13412.5,1.092515,0.23,854,851,3,3,True,all_resolved,user_exit,True,True,True,"[7, 12, 18]",[],0
scene-92002,hrl_heuristic,4,3,3,12037.845,4012.615,12037.845,5.932461,0.332,850,847,3,3,True,all_resolved,user_exit,True,True,True,"[1, 16, 17]",[],0
scene-92002,directional,4,3,3,13136.114,4378.705,13136.114,1.064197,0.245,851,848,3,3,True,all_resolved,user_exit,True,True,True,"[1, 16, 17]",[],0
1 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
2 scene-92000 hrl_heuristic 4 3 3 12779.703 4259.901 12779.703 6.558622 0.347 892 889 3 3 True all_resolved user_exit True True True [1, 6, 16] [] 0
3 scene-92000 directional 4 3 3 13780.377 4593.459 13780.377 1.031098 0.252 886 883 3 3 True all_resolved user_exit True True True [1, 6, 16] [] 0
4 scene-92001 hrl_heuristic 4 3 3 12059.045 4019.682 12059.045 6.361647 0.32 848 845 3 3 True all_resolved user_exit True True True [7, 12, 18] [] 0
5 scene-92001 directional 4 3 3 13412.5 4470.833 13412.5 1.092515 0.23 854 851 3 3 True all_resolved user_exit True True True [7, 12, 18] [] 0
6 scene-92002 hrl_heuristic 4 3 3 12037.845 4012.615 12037.845 5.932461 0.332 850 847 3 3 True all_resolved user_exit True True True [1, 16, 17] [] 0
7 scene-92002 directional 4 3 3 13136.114 4378.705 13136.114 1.064197 0.245 851 848 3 3 True all_resolved user_exit True True True [1, 16, 17] [] 0
output/hrl/p1-multiround-q4/aggregate.json
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{
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}
output/hrl/p1-multiround-q4/manifest.json
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{
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output/hrl/p1-multiround-q4/results.json
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[
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output/hrl/p1-multiround-q4/run.log
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{"event": "run_start", "scene": "scene-92000", "variant": "hrl_heuristic"}
{"event": "run_done", "scene_id": "scene-92000", "variant": "hrl_heuristic", "cleared_count": 3, "virtual_time_s": 12779.703, "valid": true, "program_run_time_s": 6.558622}
{"event": "run_start", "scene": "scene-92000", "variant": "directional"}
{"event": "run_done", "scene_id": "scene-92000", "variant": "directional", "cleared_count": 3, "virtual_time_s": 13780.377, "valid": true, "program_run_time_s": 1.031098}
{"event": "run_start", "scene": "scene-92001", "variant": "hrl_heuristic"}
{"event": "run_done", "scene_id": "scene-92001", "variant": "hrl_heuristic", "cleared_count": 3, "virtual_time_s": 12059.045, "valid": true, "program_run_time_s": 6.361647}
{"event": "run_start", "scene": "scene-92001", "variant": "directional"}
{"event": "run_done", "scene_id": "scene-92001", "variant": "directional", "cleared_count": 3, "virtual_time_s": 13412.5, "valid": true, "program_run_time_s": 1.092515}
{"event": "run_start", "scene": "scene-92002", "variant": "hrl_heuristic"}
{"event": "run_done", "scene_id": "scene-92002", "variant": "hrl_heuristic", "cleared_count": 3, "virtual_time_s": 12037.845, "valid": true, "program_run_time_s": 5.932461}
{"event": "run_start", "scene": "scene-92002", "variant": "directional"}
{"event": "run_done", "scene_id": "scene-92002", "variant": "directional", "cleared_count": 3, "virtual_time_s": 13136.114, "valid": true, "program_run_time_s": 1.064197}
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output/hrl/p1-multiround-q4/runs/scene-92000/directional/session.jsonl
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output/hrl/p1-resume-smoke/high_curve_q3_env0.monitor.csv
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output/hrl/p1-smoke/q3.json
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output/hrl/p1-smoke/q3.zip
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output/hrl/p1-smoke/train.log
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output/hrl/p1-subproc-smoke/high_curve_q3_env0.monitor.csv
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Can't render this file because it contains an unexpected character in line 1 and column 3.
output/hrl/p1-subproc-smoke/high_curve_q3_env1.monitor.csv
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#{"t_start": 1789128198.7435038, "env_id": "None"}
r,l,t
Can't render this file because it contains an unexpected character in line 1 and column 3.
output/hrl/p1-subproc-smoke/q3.json
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"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。",
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output/hrl/p1-subproc-smoke/q3.zip
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output/hrl/p1-tiny-time/high_curve_q3_env0.monitor.csv
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#{"t_start": 1789127784.936411, "env_id": "None"}
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Can't render this file because it contains an unexpected character in line 1 and column 3.
output/hrl/p1-tiny-time/q3.json
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{
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"monitor_files": [
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],
"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。",
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},
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}

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