diff --git a/README.md b/README.md index 4d4badd..eb872ef 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,7 @@ - [协议规则表、字段表与状态机（P0 交付）](docs/协议规则表与状态机.md) - [问题一完整解答](docs/问题一解答.md) - [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) - [论文](docs/paper/main.tex) ## 项目结构 @@ -21,10 +22,13 @@ src/mcm26b/ ├── belief/ # P3 逐频道候选集合 K_c：保守外包多边形 + 半平面裁剪（已完工） ├── coverage/ # P4 49点覆盖账本（网格生成 + 逐频道记录） ├── planner/ # P3 保证接收盘选点器 / P4 全向闭环 / P5 混合源定向闭环（已完工） +│ # P6b RL 接入：planner/rl_loop.py（PPO 选点，惰性加载 torch） +├── rl/ # P6b 强化学习扩展：训练环境、观测/候选特征、PPO 模型加载（可选依赖） ├── evaluation/ # 日志复算与统计（已完工） └── harness/ # 测试侧适配：评测机<->Transport 内存回环、故障注入、回放、场景 tests/ # pytest（含解耦守卫：运行时零导入 referee + 规则表不漂移） -scripts/ # 采集、基准、可视化与校核脚本（含问题四批量仿真 benchmark_q4.py） +scripts/ # 采集、基准、可视化与校核脚本（含问题四批量仿真 benchmark_q4.py、 + # RL 训练 train_rl.py 与 RL 配对消融 benchmark_rl.py） output/ examples/ # 演练证据、算例与生成产物 ``` @@ -64,11 +68,20 @@ Python ≥ 3.10，运行时依赖仅 `requests`；测试/自检另需 `pytest` ```bash uv sync # 安装（可编辑挂载本包与 referee） +uv sync --group rl --group dev # 追加 RL 依赖：torch(CUDA)+stable-baselines3+gymnasium uv run pytest # 全部测试（含 referee 90 项 + 解耦守卫） uv run python -m referee # 起本地评测机（可选，配合 WebUI 回放） uv run mcm26b sim-check # P0 自检：复现附件计时示例 105/111/194/199 uv run mcm26b run --robot-id 2026001 --self-test # 问题3自检：随机全向场景闭环（不连官方） uv run mcm26b run --robot-id 2026001 --self-test --problem 4 # 问题4自检：混合定向源场景 +# RL 策略（需先安装 rl 依赖组并训练模型；详见 docs/algo-rl.md）： +uv run python scripts/train_rl.py --problem 3 --timesteps 120000 # PPO + CUDA 训练 Q3 选点 +uv run python scripts/pretrain_rl_expert.py --problem 4 --episodes 3000 # 自建模拟器专家行为克隆 +uv run python scripts/train_rl.py --problem 4 --timesteps 40000 --init output/rl/q4_ppo_bc_init.zip +uv run mcm26b run --robot-id 2026001 --self-test --strategy rl # 问题3 RL 闭环 +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 # 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 @@ -134,6 +147,7 @@ client.exit() | P4 问题3全向闭环（49点覆盖、定位队列、清除、停止） | ✅ 官方问题3演练单案例通过（10/10全清，[验证报告](output/q3/practice/20260911-port2026/report.md)） | | P5 问题4定向扩展（联合约束、后备清除、预算） | ✅ 自建完成（官方演练待执行） | | P6 加速与消融 | ✅ 七点批量（`--strategy seven`，官方演练 12/12）+ 贝叶斯闭环（`--strategy bayes` 默认，自建消融 −30.0%，[算法说明](docs/algo-3-bayes.md)） | +| P6b RL 选点 | ✅ PPO+CUDA 训练环境/闭环接入/官方+自建演练验证/专家 BC 初始化（Q3 `--strategy rl`，Q4 `--q4-strategy rl`，[算法说明](docs/algo-rl.md)） | ## 采集与正式测试约束 diff --git a/docs/B题建模分析与实施计划.md b/docs/B题建模分析与实施计划.md index 2e90430..c1857fd 100644 --- a/docs/B题建模分析与实施计划.md +++ b/docs/B题建模分析与实施计划.md @@ -194,6 +194,7 @@ AI 的适合用法是把这些模块逐一变成可测试程序、生成边界 | P4 全向闭环 | 49点覆盖记录、定位队列、清除、停止、日志统计 | 10/16源、边界源和错误响应案例；官方演练全清并正常退出 | 自建闭环已完成：`planner.OmnidirectionalLoop`（10/16/0 源、近边界源、预算耗尽、故障注入测试通过）；官方演练全清待团队执行 | | P5 定向扩展 | no_signal联合约束、覆盖发现、有限清除后备与预算 | 朝外边界源不漏；真源不误删；格数与退出预算检查 | 已实现：`planner.DirectionalExtension`（49点覆盖对定向源成立、保证接收盘内 no_signal 记为观测到定向、对跖+环探梯子、`fallback_clear_cells` 有限圆盘覆盖后备清除带格数/预算双硬顶）；`tests/test_loop_q4.py` 全过；官方混合源演练待执行 | | P6 加速消融 | 共享测点、自适应覆盖、目标排序、提前清除 | 配对案例全清不退化，时间/请求数改善可复算 | 可删减；闭环基线数据已可用于消融对照 | +| P6b RL 选点 | 问题3/4 测量点选择的 PPO+CUDA 训练环境与闭环接入 | 自建全清；与手写策略配对消融有可复算报告 | 已完成：`scripts/train_rl.py`/`benchmark_rl.py`，模型 `output/rl/q{3,4}_ppo.zip`，说明 `docs/algo-rl.md` | | P7 正式与论文 | 固定版本、论文表1、日志原名、上传核查 | 问题3/4各3份正式记录与程序版本对应 | 团队后续安排；本任务不执行正式测试 | 按2026年9月10日起的剩余时间，建议10日补齐P1并衔接P3，11日完成P4自建边界案例与全向演练，12日完成P5及混合源演练；未达验收就删减P6并优先修正闭环。演练不限3次，正式机会为问题3和4各3次。团队在版本稳定后自行安排正式记录，留出失败恢复和论文整理时间。附件建议9月13日15:30前完成正式测试，17:30后不能启动新的演练或正式测试；已进入上传队列的日志仍会继续上传。此处引用所给附件安排，运行前核对模拟器界面公告；这份排期不改变当前禁止使用正式测试的限制。 diff --git a/docs/algo-rl.md b/docs/algo-rl.md new file mode 100644 index 0000000..feda313 --- /dev/null +++ b/docs/algo-rl.md @@ -0,0 +1,156 @@ +# 问题3/4 强化学习选点（PPO + CUDA） + +## 0. 摘要 + +本项目在保持 P0–P5 保守性与可解释证明不变的前提下，为问题3（全向源）与 +问题4（定向源）增加了一个可选的**强化学习测量点选择策略**： + +- 把“下一检测点去哪”建模为离散决策马尔可夫决策过程（MDP）； +- 状态由逐频道信念的几何特征 + 60 个候选检测点的几何特征组成（401 维）； +- 动作为 60 选 1（保证接收盘环候选、对称交会候选、定向遮挡环探候选）； +- 使用 Stable-Baselines3 的 PPO 训练，MLP 策略与价值网络在 **CUDA GPU** + （本机 RTX 4060 Laptop, CUDA 12.8 / torch 2.11.0+cu128）上更新； +- 训练环境基于自建评测机 `referee`（内存回环，与真实 HTTP 协议同边界）； +- 运行闭环仅替换既有选点排序器，覆盖证明、清除证书、定向后备清除全部保留。 + +结论（自建配对消融，5 个种子）：Q3 用 12 万步随机初始化 PPO（与 `bayes` +基本持平，+1.18%）；Q4 用**专家行为克隆初始化 + PPO 微调**，混合源平均 +虚拟时间相对 `directional` 基线降低 **7.16%**（14250.1s → 13230.2s）。 +Q3 的 BC 初始化在消融中反而退化，因此保留随机初始化模型；Q4 的 BC 初始化 +是当前最优。官方演练验证见 §5：两次 Q4 演练（3 定向混合源、10 定向全定向 +场景）与一次 Q3 演练均全清，且通过离线审计。 + +## 1. 环境搭建（CUDA） + +```powershell +# 安装可选依赖组 rl（PyTorch CUDA 12.8 + stable-baselines3 + gymnasium） +uv sync --group rl --group dev + +# 验证 CUDA +uv run python -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0))" +``` + +`pyproject.toml` 中 `[tool.uv.sources] torch = { index = "pytorch-cu128" }` +与 `[[tool.uv.index]]` 指向 PyTorch 官方 CUDA 12.8 轮子；下载约 2.6 GB， +首次同步建议 `$env:UV_HTTP_TIMEOUT='300'`。默认 PyPI 的 Windows torch 轮子 +为 `+cpu`，不能直接训练。 + +## 2. MDP 定义 + +训练环境：`src/mcm26b/rl/env.py::LocalizationEnv`。 + +| 项 | 定义 | +| --- | --- | +| 初始状态 | 自建模拟器随机布设一个源（Q3 全向；Q4 定向比例可调，训练默认 1.0），机器狗完成一次保证得到 `direction` 的首测 | +| 观测 | 401 维：21 个全局特征 + 20 维频道 one-hot + 60×6 维候选点特征（见 `features.py`） | +| 动作 | `Discrete(60)`：0=候选集 MEC 圆心，1..36=保证接收盘 3 环×12 方向，37..51=对称交会 15 点，52..59=定向遮挡环探 8 点 | +| 转移 | 按动作位置调用 `/measure`，用 `BeliefState` 保守更新候选集；Q4 保证接收盘内 `no_signal` 记为朝向遮挡并进入 `blocked` 序列；证书达标后自动 `/clear`；遮挡梯子用尽后模拟 20m 网格后备清除 | +| 奖励 | `-Δvirtual_time/100`（移动+检测+换频+清除真实耗时）、候选半径收缩加成、首次遮挡证据 +2、重复测点 -0.75、清除成功 +10、失败/截断负奖励 | + +约束说明：候选点全部来自既有几何生成器（`planner.candidate_points`、 +`planner.safe_disk_radius`、定向环探梯子），RL 不生成新的空间候选；它只做 +排序。因此 RL 决策不能破坏“保证接收盘内必收到方向”与清除证书的数学保证。 + +## 3. 训练与自建模拟器逻辑利用 + +```powershell +# 1) 用自建模拟器上的手写专家生成 (观测, 动作) 并行为克隆 +uv run python scripts/pretrain_rl_expert.py --problem 3 --episodes 3000 +uv run python scripts/pretrain_rl_expert.py --problem 4 --episodes 3000 + +# 2) 从 BC 初始化继续 PPO 微调（CUDA） +uv run python scripts/train_rl.py --problem 4 --timesteps 40000 \ + --init output/rl/q4_ppo_bc_init.zip + +# Q3 保留随机初始化长训练版本： +uv run python scripts/train_rl.py --problem 3 --timesteps 120000 +``` + +专家逻辑与自建模拟器同源：Q3 用 worst-posterior 选点器，Q4 用对跖/环探 +遮挡梯子；`pretrain_rl_expert.py` 在 `LocalizationEnv` 上记录每个状态的 +专家动作，训练与 PPO policy_net/action_net 同构的 MLP，再把权重拷入 PPO +作为初始化。产物位于 `output/rl/bc/` 与 `output/rl/q{3,4}_ppo_bc_init.zip`。 + +实测：Q3 专家数据 5141 条、BC 验证准确率 38.9%，微调后自建消融退化 +（保留 12 万步随机初始化模型）；Q4 专家数据 14181 条、BC 验证准确率 +65.3%，微调后自建消融从 −3.84% 提升到 **−7.16%**。这说明自建模拟器逻辑 +在遮挡分支上可直接转化为 RL 的强先验，而全向分支的随机 PPO 已足够。 + +输出：`output/rl/q3_ppo.zip`、`output/rl/q4_ppo.zip` 及同名 JSON 报告； +训练脚本先打印 `train_start`（含 device/gpu 字段），再执行 PPO 并在独立 +种子场景上确定性评估成功率、平均步数与平均回报。Q4 4 万步微调约 236s； +Q3 12 万步约 625s（RTX 4060 Laptop，2 个 CPU 环境）。 + +## 4. 运行闭环接入 + +- Q3：`uv run mcm26b run --robot-id 2026001 --self-test --strategy rl` + `RLBayesLoop` 继承 `BayesLoop`（七点覆盖 + 网格粒子滤波 + 滚动 2-opt）， + 仅 `_select_point` 改由 PPO 输出候选编号。 +- Q4：`uv run mcm26b run --robot-id 2026001 --self-test --problem 4 --q4-strategy rl` + `RLDirectionalLoop` 继承 `DirectionalExtension`（49 点覆盖 + 遮挡梯子 + + 20m 有限圆盘后备清除），仅 `_next_probe` 改由 PPO 输出。 +- 模型加载时校验观测 401 维 / 动作 60 维，失配直接拒绝运行。 +- 默认运行路径不导入 torch/referee；只有显式选择 `rl` 才惰性导入，项目 + 运行时依赖仍保持 `requests` 一项。 + +## 5. 消融基准 + +```powershell +uv run python scripts/benchmark_rl.py --seeds 5 +``` + +同种子同场景配对比较，结果写入 `output/rl/benchmark.json`。当前版本： + +| 场景 | 基线 | 平均虚拟时间（基线 / RL） | 相对差 | 全清 | +| --- | --- | --- | --- | --- | +| Q3（10 源全向 ×5 种子，scratch PPO） | `bayes` | 3088.3s / 3124.8s | +1.18% | 5/5 | +| Q4（10 源、3 定向 ×5 种子，BC+PPO） | `directional` | 14250.1s / 13230.2s | −7.16% | 5/5 | + +训练局评估：Q3 单源 40 局成功率 100%、平均 3.55 步；Q4 定向单源 40 局 +成功率 90%、平均 4.45 步（其余由后备清除/二次定位兜底）。随机策略对照： +Q3 平均回报 13.28 vs PPO 17.71；Q4 平均回报 8.72 vs BC+PPO 12.62。 + +### 5.1 官方模拟器演练验证（仅演练，未触碰正式测试） + +使用目录内官方 `jammers-simulator-full` 的**演练测试**（不限次数），通过 +官方 HTTP 接口运行同一闭环；官方产物按原名归档并离线审计： + +| 问题 | 案例 | 场景真值（演练结束后可见） | 结果 | 虚拟时间 | 请求数 | 审计 | +| --- | --- | --- | --- | --- | --- | --- | +| Q3 | `F38Y-9Q9J-B82V-CB9Z` | 13 全向 | 13/13 全清 | 3538.8s | 124 | ✅ | +| Q4 | `X43X-VMRF-8GRZ-3SYA` | 7 全向 + 3 定向 | 10/10 全清 | 14003.1s | 619 | ✅ | +| Q4(BC) | `BXSA-TFQ3-2DJ6-DHH7` | **10 定向** | 10/10 全清 | 14562.8s | 691 | ✅ | + +前两次为随机初始化模型，第三次为 BC 初始化微调模型。两个 Q4 官方案例的 +源构成不同（3 定向 vs 10 定向），**虚拟时间不可直接相减比较**；可确认的 +是 RL 在官方混合源与全定向源演练中都能完成全部清除，且 +`all_timestamps_match_microsecond_recomputation`、官方 `.jlog` 的 +SHA-256 校验全部通过。归档目录：`output/rl/official-practice/`。 + +### 5.2 自建 referee 演练验证 + +`uv run mcm26b run --local-judge` 走自建 referee 的 `q3_practice` / +`q4_practice` 模块（与官方相同的演练生命周期，真值在结束后可见）： + +| 问题 | 案例 | 场景 | 结果 | 虚拟时间 | 归档 | +| --- | --- | --- | --- | --- | --- | +| Q3 | `FKUY-K8KJ-RXRL-FMBM` | 10 全向 | 10/10 全清 | 3317.6s | `output/rl/local-practice/q3/20260911-rl-01` | +| Q4 | `3J8T-L9GY-HRGL-LYVZ` | 7 全向 + 3 定向 | 10/10 全清 | 11942.8s | `output/rl/local-practice/q4/20260911-rl-01` | +| Q4(BC) | `VM2G-UVC6-4N7R-TVGW` | 7 全向 + 3 定向 | 10/10 全清 | 12520.8s | `output/rl/local-practice/q4/20260911-rl-bc-01` | + +不同案例仍不可直接比较；成对可比的提升证据是 §5 的 5 种子同场景消融。 + +## 6. 结论与边界 + +- RL 已接入问题3/4 完整闭环，并在自建 referee 演练与官方模拟器演练中 + 全清验证；这是方法验证，不是对正式成绩的承诺。 +- 利用自建模拟器逻辑做专家行为克隆，对 Q4 遮挡分支带来明确收益 + （−3.84% → −7.16%）；Q3 全向分支空间有限，BC 初始化无收益，保留随机 + 初始化的长训练模型。 +- 训练环境使用自建场景假设（源均匀分布、逐点固定误差场、定向源朝向随机）； + 官方隐藏分布未必相同。正式测试仍应默认使用经过演练验证的手写策略； + `--strategy rl` / `--q4-strategy rl` 适合消融、方法对比与后续调参。 +- 官方演练仅使用“演练测试”按钮与开放接口，从未点击“正式测试”；官方 + `.jlog/.result.json` 保持原样，未解密。 +- 保守性不变式：RL 只改变选点排序，不改变覆盖账本、候选集更新、清除证书 + 与后备清除的数学证明；所有策略均以 `/exit` 与完整日志收尾。 diff --git a/docs/paper/main.pdf b/docs/paper/main.pdf index 5604dec..4b774ac 100644 Binary files a/docs/paper/main.pdf and b/docs/paper/main.pdf differ diff --git a/output/rl/bc/q3_bc.json b/output/rl/bc/q3_bc.json new file mode 100644 index 0000000..c8981ac --- /dev/null +++ b/output/rl/bc/q3_bc.json @@ -0,0 +1,8 @@ +{ + "problem": 3, + "samples": 5141, + "valid_accuracy": 0.38910505175590515, + "dataset": "output\\rl\\bc\\q3_expert.npz", + "bc_model": "output\\rl\\bc\\q3_bc.pt", + "ppo_init": "output\\rl\\q3_ppo_bc_init.zip" +} \ No newline at end of file diff --git a/output/rl/bc/q3_bc.pt b/output/rl/bc/q3_bc.pt new file mode 100644 index 0000000..fb06458 Binary files /dev/null and b/output/rl/bc/q3_bc.pt differ diff --git a/output/rl/bc/q3_expert.npz b/output/rl/bc/q3_expert.npz new file mode 100644 index 0000000..074b642 Binary files /dev/null and b/output/rl/bc/q3_expert.npz differ diff --git a/output/rl/bc/q4_bc.json b/output/rl/bc/q4_bc.json new file mode 100644 index 0000000..c5075c3 --- /dev/null +++ b/output/rl/bc/q4_bc.json @@ -0,0 +1,8 @@ +{ + "problem": 4, + "samples": 14181, + "valid_accuracy": 0.6530324816703796, + "dataset": "output\\rl\\bc\\q4_expert.npz", + "bc_model": "output\\rl\\bc\\q4_bc.pt", + "ppo_init": "output\\rl\\q4_ppo_bc_init.zip" +} \ No newline at end of file diff --git a/output/rl/bc/q4_bc.pt b/output/rl/bc/q4_bc.pt new file mode 100644 index 0000000..3415406 Binary files /dev/null and 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b/output/rl/local-practice/q3/20260911-rl-01/cli-output.txt new file mode 100644 index 0000000..4f0a6bf --- /dev/null +++ b/output/rl/local-practice/q3/20260911-rl-01/cli-output.txt @@ -0,0 +1,1739 @@ +C:\Users\HelixCopex\code\MATH\.venv\Lib\site-packages\stable_baselines3\common\on_policy_algorithm.py:150: UserWarning: You are trying to run PPO on the GPU, but it is primarily intended to run on the CPU when not using a CNN policy (you are using ActorCriticPolicy which should be a MlpPolicy). See https://github.com/DLR-RM/stable-baselines3/issues/1245 for more info. You can pass `device='cpu'` or `export CUDA_VISIBLE_DEVICES=` to force using the CPU.Note: The model will train, but the GPU utilization will be poor and the training might take longer than on CPU. + warnings.warn( +[一键评测] 机器狗接口端口 2026 已被占用：回车改用空闲端口 58271，或输入其他端口：[一键评测] 案例编码 FKUY-K8KJ-RXRL-FMBM · 问题3演练测试 · 接口 http://127.0.0.1:58271 +{ + "completed": true, + "stop_reason": "all_resolved", + "exit_reason": "user_exit", + "virtual_time_s": 3317.563, + "accounted_virtual_time_s": 3317.563, + "persistent_search": false, + "cleared_channels": [ + 1, + 2, + 3, + 5, + 6, + 9, + 10, + 12, + 17, + 19 + ], + "absent_channels": [ + 4, + 7, + 8, + 11, + 13, + 14, + 15, + 16, + 18, + 20 + ], + "unresolved_channels": [], + "measures": 125, + "clears": 10, + "clear_success": 10, + "total_requests": 135, + "move_distance_m": 12662.8, + "avg_locate_clear_time_s": 331.756, + "step_totals": { + "count": 137, + "real_elapsed_total_s": 0.53, + "real_since_enter_total_s": 1.745, + "virtual_move_s": 2532.563, 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See https://github.com/DLR-RM/stable-baselines3/issues/1245 for more info. 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See https://github.com/DLR-RM/stable-baselines3/issues/1245 for more info. 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"directional_count": 3 + }, + "all_cleared": true, + "log": { + "path": "output\\rl\\local-practice\\q4\\20260911-rl-bc-01\\judge\\sessions\\VM2G-UVC6-4N7R-TVGW\\log.jsonl", + "size_bytes": 229611, + "over_upload_limit": false + } + } +} diff --git a/output/rl/official-practice/README.md b/output/rl/official-practice/README.md new file mode 100644 index 0000000..3ea1fd0 --- /dev/null +++ b/output/rl/official-practice/README.md @@ -0,0 +1,15 @@ +# RL 官方模拟器演练验证归档 + +本目录只归档**官方模拟器“演练测试”**（问题3/4），从未执行正式测试。 + +- Q3 `20260911-rl-01`：案例 `F38Y-9Q9J-B82V-CB9Z`，13 全向源，13/13 全清， + 虚拟时间 3538.811s，124 请求；`verification.json` 全部通过。 +- Q4 `20260911-rl-01`：案例 `X43X-VMRF-8GRZ-3SYA`，7 全向 + 3 定向， + 10/10 全清，虚拟时间 14003.086s，619 请求（随机初始化 PPO）。 +- Q4 `20260911-rl-bc-01`：案例 `BXSA-TFQ3-2DJ6-DHH7`，**10 定向源**， + 10/10 全清，虚拟时间 14562.817s，691 请求（自建模拟器专家行为克隆初始化 + 后 PPO 微调）。 + +每个目录保留：官方 `.result.json/.jlog/.psum` 原文件、我方 `session.jsonl`、 +`summary.json`、`verification.json` 与 `manifest.json`。两个 Q4 案例源构成 +不同，虚拟时间不直接可比；共同结论是 RL 闭环在官方演练中均可全部清除。 diff --git a/output/rl/official-practice/q3/20260911-rl-01/cli-output.txt b/output/rl/official-practice/q3/20260911-rl-01/cli-output.txt new file mode 100644 index 0000000..9e5bedf --- /dev/null +++ b/output/rl/official-practice/q3/20260911-rl-01/cli-output.txt @@ -0,0 +1,1782 @@ +C:\Users\HelixCopex\code\MATH\.venv\Lib\site-packages\stable_baselines3\common\on_policy_algorithm.py:150: UserWarning: You are trying to run PPO on the GPU, but it is primarily intended to run on the CPU when not using a CNN policy (you are using ActorCriticPolicy which should be a MlpPolicy). See https://github.com/DLR-RM/stable-baselines3/issues/1245 for more info. You can pass `device='cpu'` or `export CUDA_VISIBLE_DEVICES=` to force using the CPU.Note: The model will train, but the GPU utilization will be poor and the training might take longer than on CPU. + warnings.warn( +{ + "completed": true, + "stop_reason": "all_resolved", + "exit_reason": "user_exit", + "virtual_time_s": 3538.811, + "accounted_virtual_time_s": 3538.811, + "persistent_search": false, + "cleared_channels": [ + 2, + 3, + 4, + 5, + 6, + 8, + 9, + 10, + 11, + 14, + 15, + 19, + 20 + ], + "absent_channels": [ + 1, + 7, + 12, + 13, + 16, + 17, + 18 + ], + "unresolved_channels": [], + "measures": 111, + "clears": 13, + "clear_success": 13, + "total_requests": 124, + "move_distance_m": 14089.1, + "avg_locate_clear_time_s": 272.216, + "step_totals": { + "count": 126, + "real_elapsed_total_s": 0.895, + "real_since_enter_total_s": 2.319, + "virtual_move_s": 2817.811, + "virtual_switch_s": 101.0, + "virtual_measure_action_s": 555.0, + "virtual_clear_action_s": 65.0, + 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See https://github.com/DLR-RM/stable-baselines3/issues/1245 for more info. 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"limits": "仅验证本官方演练案例；不触碰正式测试，不解读官方加密日志内容。" +} diff --git a/output/rl/official-practice/q4/20260911-rl-bc-01/cli-output.txt b/output/rl/official-practice/q4/20260911-rl-bc-01/cli-output.txt new file mode 100644 index 0000000..77973d9 --- /dev/null +++ b/output/rl/official-practice/q4/20260911-rl-bc-01/cli-output.txt @@ -0,0 +1,1237 @@ +C:\Users\HelixCopex\code\MATH\.venv\Lib\site-packages\stable_baselines3\common\on_policy_algorithm.py:150: UserWarning: You are trying to run PPO on the GPU, but it is primarily intended to run on the CPU when not using a CNN policy (you are using ActorCriticPolicy which should be a MlpPolicy). See https://github.com/DLR-RM/stable-baselines3/issues/1245 for more info. 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"channel": 4, + "action": 54, + "position": [ + 1515.876, + -603.897 + ], + "blocked_count": 3, + "virtual_time_s": 9075.472566 + }, + { + "channel": 12, + "action": 32, + "position": [ + 1284.178, + -427.213 + ], + "blocked_count": 0, + "virtual_time_s": 9221.535541 + }, + { + "channel": 12, + "action": 54, + "position": [ + 1991.284, + -553.754 + ], + "blocked_count": 1, + "virtual_time_s": 9286.806865 + }, + { + "channel": 12, + "action": 37, + "position": [ + 1477.378, + -508.377 + ], + "blocked_count": 0, + "virtual_time_s": 9435.474752 + } + ], + "robot_id": "202608023016", + "steps_truncated": true +} diff --git a/output/rl/official-practice/q4/20260911-rl-bc-01/verification.json b/output/rl/official-practice/q4/20260911-rl-bc-01/verification.json new file mode 100644 index 0000000..a0d9469 --- /dev/null +++ b/output/rl/official-practice/q4/20260911-rl-bc-01/verification.json @@ -0,0 +1,39 @@ +{ + "passed": true, + "source": "official_practice", + "strategy": "rl_q4", + "problem": 4, + "case_code": "BXSA-TFQ3-2DJ6-DHH7", + "official_jammer_count": 10, + "directional_jammer_count": 10, + "cleared_channels": [ + 2, + 4, + 5, + 6, + 8, + 11, + 12, + 13, + 14, + 17 + ], + "accepted_requests_including_enter_exit": 693, + "measures": 670, + "clear_attempts": 21, + "clear_successes": 10, + "virtual_time_s": 14562.81673, + "real_elapsed_server_s": 6.39, + "checks": { + "official_problem_matches_summary": true, + "official_package_sha256_matches": true, + "normal_enter_and_exit": true, + "responses_within_official_run_window": true, + "unique_accepted_request_ids": true, + "all_official_sources_cleared": true, + "summary_completed_and_consistent": true, + "all_timestamps_match_microsecond_recomputation": true, + "within_real_and_virtual_limits": true + }, + "limits": "仅验证本官方演练案例；不触碰正式测试，不解读官方加密日志内容。" +} diff --git a/output/rl/q3_ppo.json b/output/rl/q3_ppo.json new file mode 100644 index 0000000..e8dc0c1 --- /dev/null +++ b/output/rl/q3_ppo.json @@ -0,0 +1,15 @@ +{ + "problem": 3, + "timesteps": 120000, + "seed": 20260911, + "device": "cuda", + "directional_probability": 0.0, + "wall_seconds": 624.9, + "model": "output\\rl\\q3_ppo.zip", + "init": null, + "episodes": 40, + "success_rate": 1.0, + "mean_steps": 3.55, + "mean_episode_reward": 17.714614359422605, + "successes": 40 +} diff --git a/output/rl/q3_ppo.zip b/output/rl/q3_ppo.zip new file mode 100644 index 0000000..f88eec3 Binary files /dev/null and b/output/rl/q3_ppo.zip differ diff --git a/output/rl/q3_ppo_bc.json b/output/rl/q3_ppo_bc.json new file mode 100644 index 0000000..f66d418 --- /dev/null +++ b/output/rl/q3_ppo_bc.json @@ -0,0 +1,15 @@ +{ + "problem": 3, + "timesteps": 40000, + "seed": 20260911, + "device": "cuda", + "directional_probability": 0.0, + "wall_seconds": 279.0, + "model": "output\\rl\\q3_ppo.zip", + "init": "output/rl/q3_ppo_bc_init.zip", + "episodes": 40, + "success_rate": 1.0, + "mean_steps": 2.9, + "mean_episode_reward": 17.57896344478182, + "successes": 40 +} \ No newline at end of file diff --git a/output/rl/q3_ppo_bc.zip b/output/rl/q3_ppo_bc.zip new file mode 100644 index 0000000..e180e14 Binary files /dev/null and b/output/rl/q3_ppo_bc.zip differ diff --git a/output/rl/q3_ppo_bc_init.zip b/output/rl/q3_ppo_bc_init.zip new file mode 100644 index 0000000..e9e8ab8 Binary files /dev/null and b/output/rl/q3_ppo_bc_init.zip differ diff --git a/output/rl/q3_ppo_scratch.zip b/output/rl/q3_ppo_scratch.zip new file mode 100644 index 0000000..f88eec3 Binary files /dev/null and b/output/rl/q3_ppo_scratch.zip differ diff --git a/output/rl/q4_ppo.json b/output/rl/q4_ppo.json new file mode 100644 index 0000000..ba61340 --- /dev/null +++ b/output/rl/q4_ppo.json @@ -0,0 +1,15 @@ +{ + "problem": 4, + "timesteps": 40000, + "seed": 20260911, + "device": "cuda", + "directional_probability": 1.0, + "wall_seconds": 235.8, + "model": "output\\rl\\q4_ppo.zip", + "init": "output/rl/q4_ppo_bc_init.zip", + "episodes": 40, + "success_rate": 0.9, + "mean_steps": 4.45, + "mean_episode_reward": 12.621703708323922, + "successes": 36 +} \ No newline at end of file diff --git a/output/rl/q4_ppo.zip b/output/rl/q4_ppo.zip new file mode 100644 index 0000000..c27f0a4 Binary files /dev/null and b/output/rl/q4_ppo.zip differ diff --git a/output/rl/q4_ppo_bc_init.zip b/output/rl/q4_ppo_bc_init.zip new file mode 100644 index 0000000..a8017f0 Binary files /dev/null and b/output/rl/q4_ppo_bc_init.zip differ diff --git a/output/rl/q4_ppo_scratch.zip b/output/rl/q4_ppo_scratch.zip new file mode 100644 index 0000000..53807b2 Binary files /dev/null and b/output/rl/q4_ppo_scratch.zip differ diff --git a/pyproject.toml b/pyproject.toml index b8819e6..77ea84a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,13 +15,25 @@ dev = [ # 独立评测机（自建模拟器）：仅测试侧依赖，运行时代码零导入（懒加载 + 守卫测试） "mcm26b-referee", ] +rl = [ + "gymnasium>=1.1", + "stable-baselines3>=2.6", + "torch==2.11.0", +] # uv 工作区：referee/ 是独立包（零第三方依赖、不依赖 mcm26b），双方仅通过 HTTP 协议耦合 [tool.uv.workspace] members = ["referee"] +# RL 训练使用 CUDA 12.8 版 PyTorch（本机 RTX 4060；运行时依赖保持轻量）。 +[[tool.uv.index]] +name = "pytorch-cu128" +url = "https://download.pytorch.org/whl/cu128" +explicit = true + [tool.uv.sources] mcm26b-referee = { workspace = true } +torch = { index = "pytorch-cu128" } [build-system] requires = ["hatchling"] diff --git a/scripts/benchmark_rl.py b/scripts/benchmark_rl.py new file mode 100644 index 0000000..7e5afd2 --- /dev/null +++ b/scripts/benchmark_rl.py @@ -0,0 +1,113 @@ +"""问题3/4 RL 策略与既有策略的配对自建消融。 + +同一种子、同一场景分别跑基线（Q3 bayes / Q4 directional）与 RL 变体， +比较全部清除成功、虚拟总时间、检测次数与请求总数。结果写入 +output/rl/benchmark.json。 + +用法： + uv sync --group rl --group dev + uv run python scripts/benchmark_rl.py --seeds 5 +""" + +from __future__ import annotations + +import argparse +import json +import statistics +import time +from pathlib import Path + +from mcm26b.config import ProblemConfig +from mcm26b.harness import (LocalSimTransport, mixed_scene, random_omni_scene, + referee_spec) +from mcm26b.planner import DirectionalExtension +from mcm26b.planner.bayes_loop import BayesLoop +from mcm26b.planner.rl_loop import RLBayesLoop, RLDirectionalLoop +from mcm26b.protocol.client import SimulatorClient +from referee import LocalSimulator + + +def run_one(loop_cls, sources, *, config, robot_id: str, **kwargs) -> dict: + simulator = LocalSimulator(sources, robot_id=robot_id, + spec=referee_spec(config, max_real_duration_s=3600.0), + stress_mode=True) + client = SimulatorClient(robot_id, config=config, + transport=LocalSimTransport(simulator)) + started = time.monotonic() + summary = loop_cls(client, config, **kwargs).run() \ + if kwargs else loop_cls(client, config).run() + return { + "completed": bool(summary["completed"]), + "virtual_time_s": float(summary["virtual_time_s"]), + "measures": int(summary["measures"]), + "total_requests": int(summary["total_requests"]), + "move_distance_m": float(summary["move_distance_m"]), + "wall_s": round(time.monotonic() - started, 3), + } + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--seeds", type=int, default=5) + parser.add_argument("--sources", type=int, default=10) + parser.add_argument("--directional", type=int, default=3) + parser.add_argument("--output", default="output/rl/benchmark.json") + args = parser.parse_args(argv) + config = ProblemConfig() + robot_id = "2026001" + report = {"config": {"seeds": args.seeds, "sources": args.sources, + "directional": args.directional}, + "q3": [], "q4": []} + + for seed in range(20260910, 20260910 + args.seeds): + omni = random_omni_scene(seed=seed, count=args.sources, config=config) + mixed = mixed_scene(seed=seed, count=args.sources, + directional=args.directional, config=config) + q3_base = run_one(BayesLoop, omni, config=config, robot_id=robot_id) + q3_rl = run_one(RLBayesLoop, omni, config=config, robot_id=robot_id) + q4_base = run_one(DirectionalExtension, mixed, config=config, + robot_id=robot_id) + q4_rl = run_one(RLDirectionalLoop, mixed, config=config, + robot_id=robot_id) + report["q3"].append({"seed": seed, "bayes": q3_base, "rl": q3_rl}) + report["q4"].append({"seed": seed, "directional": q4_base, "rl": q4_rl}) + print(json.dumps({"event": "seed_done", "seed": seed, + "q3_base_s": q3_base["virtual_time_s"], + "q3_rl_s": q3_rl["virtual_time_s"], + "q4_base_s": q4_base["virtual_time_s"], + "q4_rl_s": q4_rl["virtual_time_s"]}, + ensure_ascii=False)) + + for key, baseline in (("q3", "bayes"), ("q4", "directional")): + rows = report[key] + deltas = [row["rl"]["virtual_time_s"] - row[baseline]["virtual_time_s"] + for row in rows] + cleared = all(row["rl"]["completed"] and row[baseline]["completed"] + for row in rows) + report[key + "_summary"] = { + "mean_virtual_s": { + baseline: statistics.mean(row[baseline]["virtual_time_s"] for row in rows), + "rl": statistics.mean(row["rl"]["virtual_time_s"] for row in rows), + }, + "mean_delta_s": statistics.mean(deltas), + "relative_delta_pct": 100.0 * statistics.mean(deltas) + / statistics.mean(row[baseline]["virtual_time_s"] for row in rows), + "mean_measures": { + baseline: statistics.mean(row[baseline]["measures"] for row in rows), + "rl": statistics.mean(row["rl"]["measures"] for row in rows), + }, + "all_cleared": cleared, + } + output = Path(args.output) + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(json.dumps(report, ensure_ascii=False, indent=2), + encoding="utf-8") + print(json.dumps({"event": "benchmark_done", "path": str(output), + "q3": report["q3_summary"], + "q4": report["q4_summary"]}, + ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/pretrain_rl_expert.py b/scripts/pretrain_rl_expert.py new file mode 100644 index 0000000..7b610fd --- /dev/null +++ b/scripts/pretrain_rl_expert.py @@ -0,0 +1,238 @@ +"""用自建模拟器的既有专家逻辑做行为克隆，初始化 PPO 策略网络。 + +思路：自建 referee 的观测模型与真值逻辑既快又可复现。先在它上面用 +手写专家（Q3: worst-posterior 选点器；Q4: 遮挡梯子选点器）生成 +(观测, 专家动作) 数据集，训练一个与 PPO policy_net 同构的 MLP 分类器， +再把权重拷入 PPO 模型作为初始化；随后 scripts/train_rl.py --init 继续 +PPO 微调。这样 RL 从强先验出发，而不是从随机策略开始。 + +产物： + output/rl/bc/q3_expert.npz output/rl/bc/q3_bc.pt output/rl/q3_ppo_bc_init.zip + output/rl/bc/q4_expert.npz output/rl/bc/q4_bc.pt output/rl/q4_ppo_bc_init.zip +""" +from __future__ import annotations + +import argparse +import json +import math +import time +from pathlib import Path + +import numpy as np +import torch +from stable_baselines3 import PPO + +from mcm26b.config import ProblemConfig +from mcm26b.planner import safe_disk_radius, select_measurement_point +from mcm26b.rl._constants import NUM_ACTIONS, OBS_DIM +from mcm26b.rl.env import LocalizationEnv +from mcm26b.rl.features import candidate_actions + +HIDDEN = (128, 128) + + +def expert_action(env: LocalizationEnv) -> int: + """当前状态的专家动作编号（与训练环境候选同源，就近映射）。""" + config = env.config + channel = env._channel + region = env._belief.candidate_set(channel) + position = env._client.state.position + blocked = env._blocked + candidates = candidate_actions(region, position, config, blocked) + + if env.problem == 3: + expert_point = select_measurement_point( + region, position, config).position + else: + center, radius = region.mec() + safe = safe_disk_radius(region, config) + if not blocked: + expert_point = select_measurement_point( + region, position, config).position + elif len(blocked) == 1 and safe >= 0: + first = blocked[0] + dx, dy = center[0] - first[0], center[1] - first[1] + norm = math.hypot(dx, dy) + if norm <= 1e-9: + expert_point = (center[0] + safe, center[1]) + else: + scale = min(1.0, safe / norm) + expert_point = (center[0] + dx * scale, + center[1] + dy * scale) + else: + first = blocked[0] + base = math.atan2(first[1] - center[1], first[0] - center[0]) + angle = base + math.radians(45.0 * (len(blocked) - 1)) + expert_point = (center[0] + radius * math.cos(angle), + center[1] + radius * math.sin(angle)) + return min(range(len(candidates)), + key=lambda k: math.dist(candidates[k], expert_point)) + + +class BCMlp(torch.nn.Module): + def __init__(self): + super().__init__() + self.net = torch.nn.Sequential( + torch.nn.Linear(OBS_DIM, HIDDEN[0]), torch.nn.ReLU(), + torch.nn.Linear(HIDDEN[0], HIDDEN[1]), torch.nn.ReLU(), + torch.nn.Linear(HIDDEN[1], NUM_ACTIONS)) + + def forward(self, x): + return self.net(x) + + +def collect(problem: int, episodes: int, seed: int, config: ProblemConfig): + env = LocalizationEnv( + config, problem=problem, + directional_probability=1.0 if problem == 4 else 0.0, + max_episode_steps=16) + observations, actions = [], [] + for episode in range(episodes): + obs, info = env.reset(seed=seed + episode) + for _step in range(env.max_episode_steps + 2): + action = expert_action(env) + observations.append(obs) + actions.append(action) + obs, _reward, terminated, truncated, _info = env.step(action) + if terminated or truncated: + break + return np.asarray(observations, dtype=np.float32), \ + np.asarray(actions, dtype=np.int64) + + +def train_bc(x, y, *, epochs: int, batch_size: int, lr: float, + device: str, valid_frac: float = 0.1): + model = BCMlp().to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=lr) + count = len(x) + split = max(1, int(count * valid_frac)) + perm = torch.randperm(count) + x_t, y_t = torch.as_tensor(x, device=device), torch.as_tensor(y, device=device) + x_v, y_v = x_t[perm[:split]], y_t[perm[:split]] + x_tr, y_tr = x_t[perm[split:]], y_t[perm[split:]] + 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()) + train_acc = correct / len(x_tr) + with torch.no_grad(): + valid_acc = float((model(x_v).argmax(1) == y_v).float().mean().item()) + if epoch == 0 or (epoch + 1) % 5 == 0: + print(json.dumps({"event": "bc_epoch", "epoch": epoch + 1, + "loss": round(total / len(x_tr), 4), + "train_acc": round(train_acc, 4), + "valid_acc": round(valid_acc, 4)}, + ensure_ascii=False)) + return model, float(valid_acc) + + +def copy_into_ppo(problem: int, model: BCMlp, device: str) -> Path | None: + """把 BC 网络权重按 SB3 的 policy_net/action_net 拆分拷入 PPO。""" + ppo_path = Path(f"output/rl/q{problem}_ppo.zip") + if not ppo_path.is_file(): + return None + ppo = PPO.load(str(ppo_path), device=device) + state = {key.removeprefix("net."): value + for key, value in model.state_dict().items()} + # SB3 MlpPolicy：policy_net 只含前两个 Linear（Tanh 激活）， + # 输出层在 action_net。BC 的 Sequential 键为 0/2/4。 + policy_state = {key: value for key, value in state.items() + if key in ("0.weight", "0.bias", "2.weight", "2.bias")} + action_state = { + "weight": state["4.weight"], "bias": state["4.bias"]} + ppo.policy.mlp_extractor.policy_net.load_state_dict(policy_state) + ppo.policy.action_net.load_state_dict(action_state) + init_path = Path(f"output/rl/q{problem}_ppo_bc_init.zip") + ppo.save(str(init_path)) + return init_path + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--problem", type=int, choices=(3, 4), required=True) + parser.add_argument("--episodes", type=int, default=3000) + parser.add_argument("--epochs", type=int, default=20) + parser.add_argument("--batch-size", type=int, default=256) + parser.add_argument("--lr", type=float, default=3e-4) + parser.add_argument("--seed", type=int, default=20260911) + parser.add_argument("--device", default="auto") + parser.add_argument("--copy-only", action="store_true", + help="跳过采集/训练，只把已有 q_bc.pt 拷入 PPO 初始化") + args = parser.parse_args(argv) + + if args.device == "auto": + device = "cuda" if torch.cuda.is_available() else "cpu" + else: + device = args.device + print(json.dumps({"event": "bc_start", "problem": args.problem, + "episodes": args.episodes, "device": device, + "cuda": torch.cuda.is_available()}, + ensure_ascii=False)) + + if args.copy_only: + bc_dir = Path("output/rl/bc") + checkpoint = torch.load(bc_dir / f"q{args.problem}_bc.pt", + map_location="cpu") + model = BCMlp() + model.load_state_dict(checkpoint["state_dict"]) + init_path = copy_into_ppo(args.problem, model, device) + dataset = np.load(bc_dir / f"q{args.problem}_expert.npz", + allow_pickle=False) + report = { + "problem": args.problem, + "samples": int(len(dataset["obs"])), + "valid_accuracy": checkpoint.get("valid_acc"), + "dataset": str(bc_dir / f"q{args.problem}_expert.npz"), + "bc_model": str(bc_dir / f"q{args.problem}_bc.pt"), + "ppo_init": str(init_path) if init_path is not None else None, + } + (bc_dir / f"q{args.problem}_bc.json").write_text( + json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8") + print(json.dumps({"event": "bc_copy", "problem": args.problem, + "ppo_init": str(init_path), **report}, + ensure_ascii=False, indent=2)) + return 0 if init_path is not None else 2 + + config = ProblemConfig() + started = time.monotonic() + x, y = collect(args.problem, args.episodes, args.seed, config) + collect_s = time.monotonic() - started + bc_dir = Path("output/rl/bc") + bc_dir.mkdir(parents=True, exist_ok=True) + np.savez_compressed(bc_dir / f"q{args.problem}_expert.npz", obs=x, action=y) + + model, valid_acc = train_bc(x, y, epochs=args.epochs, + batch_size=args.batch_size, lr=args.lr, + device=device) + torch.save({"state_dict": model.state_dict(), "obs_dim": OBS_DIM, + "actions": NUM_ACTIONS, "valid_acc": valid_acc}, + bc_dir / f"q{args.problem}_bc.pt") + + # 拷入现有 PPO 的 policy_net/action_net 作为初始化（critic 保持原权重）。 + init_path = copy_into_ppo(args.problem, model, device) + report = { + "problem": args.problem, "episodes": args.episodes, + "samples": int(len(x)), "valid_accuracy": valid_acc, + "collect_seconds": round(collect_s, 1), + "train_seconds": round(time.monotonic() - started - collect_s, 1), + "dataset": str(bc_dir / f"q{args.problem}_expert.npz"), + "bc_model": str(bc_dir / f"q{args.problem}_bc.pt"), + "ppo_init": str(init_path) if init_path is not None else None, + } + (bc_dir / f"q{args.problem}_bc.json").write_text( + json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8") + print(json.dumps(report, ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/save_official_rl_summary.py b/scripts/save_official_rl_summary.py new file mode 100644 index 0000000..8443a40 --- /dev/null +++ b/scripts/save_official_rl_summary.py @@ -0,0 +1,36 @@ +"""从 Tee 输出的 CLI 文本中提取末尾 JSON，落盘为 summary.json。 + +官方演练目录由 run 命令产生 cli-output.txt（前面可能有 CUDA 警告）， +本脚本只提取最后一个完整 JSON 对象，并核对 adjacent 的官方 result.json。 +""" +import argparse +import json +from pathlib import Path + + +def extract(text: str) -> dict: + start = text.rfind("\n{") + if start < 0: + raise ValueError("未找到 JSON 对象") + return json.loads(text[start + 1:]) + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("folder", type=Path) + args = parser.parse_args() + folder = args.folder + summary = extract((folder / "cli-output.txt").read_text(encoding="utf-8")) + (folder / "summary.json").write_text( + json.dumps(summary, ensure_ascii=False, indent=2) + "\n", encoding="utf-8") + print(json.dumps({ + "folder": str(folder), "completed": summary["completed"], + "virtual_time_s": summary["virtual_time_s"], + "cleared_channels": summary["cleared_channels"], + "total_requests": summary["total_requests"], + }, ensure_ascii=False)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/train_rl.py b/scripts/train_rl.py new file mode 100644 index 0000000..132de74 --- /dev/null +++ b/scripts/train_rl.py @@ -0,0 +1,171 @@ +"""问题3/4 强化学习选点策略训练（PPO + CUDA）。 + +训练环境 mcm26b.rl.env.LocalizationEnv 与运行闭环共享 features 观测/候选 +布局。模型输出到 output/rl/q3_ppo.zip（问题3）或 q4_ppo.zip（问题4）， +由 `mcm26b run --strategy rl` / `--q4-strategy rl` 加载。 + +示例： + uv sync --group rl --group dev + uv run python scripts/train_rl.py --problem 3 --timesteps 30000 + uv run python scripts/train_rl.py --problem 4 --timesteps 30000 + uv run mcm26b run --robot-id 2026001 --self-test --strategy rl + uv run mcm26b run --robot-id 2026001 --self-test --problem 4 --q4-strategy rl +""" + +from __future__ import annotations + +import argparse +import json +import time +from pathlib import Path + +from stable_baselines3 import PPO +from stable_baselines3.common.vec_env import DummyVecEnv + +from mcm26b.config import ProblemConfig +from mcm26b.rl.env import LocalizationEnv + + +def _make_env(problem: int, seed: int, config: ProblemConfig, + directional_probability: float): + def _factory(): + env = LocalizationEnv(config, problem=problem, + directional_probability=directional_probability, + real_budget_s=3600.0) + env.reset(seed=seed) + return env + return _factory + + +def evaluate(model, problem: int, *, episodes: int = 24, + directional_probability: float = 0.5, + config: ProblemConfig = ProblemConfig()) -> dict: + """独立场景评估：确定性策略的成功率、步数与平均回报。""" + env = LocalizationEnv(config, problem=problem, + directional_probability=directional_probability) + total_reward = 0.0 + successes = 0 + steps = [] + for episode in range(episodes): + obs, _info = env.reset(seed=1_000_000 + episode) + episode_reward = 0.0 + for step in range(env.max_episode_steps + 2): + action, _state = model.predict(obs, deterministic=True) + obs, reward, terminated, truncated, info = env.step(int(action)) + episode_reward += float(reward) + if terminated or truncated: + break + total_reward += episode_reward + cleared = bool(info.get("cleared")) + successes += int(cleared) + steps.append(step + 1) + return { + "episodes": episodes, + "success_rate": successes / episodes, + "mean_steps": sum(steps) / episodes, + "mean_episode_reward": total_reward / episodes, + "successes": successes, + } + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--problem", type=int, choices=(3, 4), default=3, + help="训练问题：3=全向源选点，4=含定向遮挡梯子") + parser.add_argument("--timesteps", type=int, default=30000, + help="PPO 总环境步数") + parser.add_argument("--seed", type=int, default=20260911) + parser.add_argument("--envs", type=int, default=2, help="并行环境份数（CPU 侧）") + parser.add_argument("--directional-probability", type=float, default=None, + help="问题4训练场景中定向源比例（默认 1.0，先学遮挡分支）") + parser.add_argument("--device", default="auto", + help="auto=优先 CUDA；或 cuda/cpu") + parser.add_argument("--model", default=None, + help="模型输出路径（默认 output/rl/q_ppo.zip）") + parser.add_argument("--init", default=None, + help="初始模型路径（如 output/rl/q3_ppo_bc_init.zip；" + "加载后继续 PPO 微调）") + parser.add_argument("--eval-episodes", type=int, default=24) + parser.add_argument("--verbose", type=int, default=1) + args = parser.parse_args(argv) + directional_probability = (args.directional_probability + if args.directional_probability is not None + else (1.0 if args.problem == 4 else 0.0)) + if not 0.0 <= directional_probability <= 1.0: + parser.error("--directional-probability 须在 [0,1]") + + import torch + if args.device == "auto": + device = "cuda" if torch.cuda.is_available() else "cpu" + else: + device = args.device + if device == "cuda" and not torch.cuda.is_available(): + parser.error("--device cuda 但当前环境检测不到 CUDA GPU") + print(json.dumps({ + "event": "train_start", "problem": args.problem, + "timesteps": args.timesteps, "device": device, + "directional_probability": directional_probability, + "torch": torch.__version__, + "gpu": torch.cuda.get_device_name(0) if device == "cuda" else None, + "cuda_available": torch.cuda.is_available(), + }, ensure_ascii=False)) + + config = ProblemConfig() + vec_env = DummyVecEnv( + [_make_env(args.problem, args.seed + rank, config, + directional_probability) + for rank in range(max(1, args.envs))]) + if args.init: + model = PPO.load(args.init, env=vec_env, device=device) + else: + model = PPO( + "MlpPolicy", vec_env, + policy_kwargs={"net_arch": [128, 128]}, + n_steps=512, + batch_size=128, + n_epochs=6, + gamma=0.99, + 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=device, + verbose=args.verbose, + ) + + started = time.monotonic() + model.learn(total_timesteps=args.timesteps, + progress_bar=args.verbose > 0) + elapsed_s = time.monotonic() - started + + model_path = Path(args.model) if args.model \ + else Path("output/rl") / f"q{args.problem}_ppo.zip" + model_path.parent.mkdir(parents=True, exist_ok=True) + model.save(str(model_path)) + + evaluation = evaluate(model, args.problem, episodes=args.eval_episodes, + directional_probability=directional_probability, + config=config) + report = { + "problem": args.problem, + "timesteps": args.timesteps, + "seed": args.seed, + "device": device, + "directional_probability": directional_probability, + "wall_seconds": round(elapsed_s, 1), + "model": str(model_path), + "init": args.init, + **evaluation, + } + report_path = model_path.with_suffix(".json") + report_path.write_text(json.dumps(report, ensure_ascii=False, indent=2), + encoding="utf-8") + print(json.dumps(report, ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/verify_rl_practice.py b/scripts/verify_rl_practice.py new file mode 100644 index 0000000..ff83282 --- /dev/null +++ b/scripts/verify_rl_practice.py @@ -0,0 +1,123 @@ +"""Offline audit of official RL practice runs (Q3/Q4); never connects to simulator. + +Checks the copied official .result.json/.jlog, our session.jsonl and summary.json: +package sha256, enter/exit, accepted timestamps inside official window, unique +request ids, clear-success channel count == official jammer_count, summary +consistency, and virtual time recomputation with integer microsecond accumulation. +""" +from __future__ import annotations + +import argparse +import hashlib +import json +import math +from datetime import datetime +from pathlib import Path + + +def save_json(path: Path, value: dict) -> None: + path.write_text(json.dumps(value, ensure_ascii=False, indent=2, + allow_nan=False) + "\n", encoding="utf-8") + + +def audit(folder: Path) -> dict: + records = [json.loads(line) for line in + (folder / "session.jsonl").read_text(encoding="utf-8").splitlines()] + events = [e for e in records if e.get("event") == "accepted"] + summary = json.loads((folder / "summary.json").read_text(encoding="utf-8")) + official_paths = list(folder.glob("*.result.json")) + if len(official_paths) != 1 or not events: + raise ValueError("Expected one official result and nonempty session evidence") + official = json.loads(official_paths[0].read_text(encoding="utf-8")) + package = official_paths[0].with_name( + official_paths[0].name.replace(".result.json", ".jlog")) + checks: dict = {} + + checks["official_problem_matches_summary"] = ( + official["problem_no"] in (3, 4) + and summary["strategy"] in ("rl", "rl_q4") + and 10 <= official["jammer_count"] <= 16 + and official["omnidirectional_jammer_count"] + official["directional_jammer_count"] + == official["jammer_count"]) + checks["official_package_sha256_matches"] = ( + hashlib.sha256(package.read_bytes()).hexdigest() == official["package_sha256"]) + checks["normal_enter_and_exit"] = ( + events[0]["path"] == "/enter" and events[-1]["path"] == "/exit" + and events[-1]["response"].get("exit_reason") == "user_exit") + start_ms = datetime.fromisoformat(official["window_started_at_utc"]).timestamp() * 1000 + end_ms = datetime.fromisoformat(official["ended_at_utc"]).timestamp() * 1000 + checks["responses_within_official_run_window"] = all( + start_ms <= e["response"]["real_timestamp_ms"] <= end_ms + 1 for e in events) + ids = [e["request"]["request_id"] for e in events] + checks["unique_accepted_request_ids"] = len(ids) == len(set(ids)) + clears = [e for e in events if e["path"] == "/clear"] + successes = [e for e in clears if e["response"].get("clear_result") == "success"] + cleared = sorted({e["request"]["channel"] for e in successes}) + checks["all_official_sources_cleared"] = len(cleared) == official["jammer_count"] + checks["summary_completed_and_consistent"] = ( + bool(summary["completed"]) + and sorted(summary["cleared_channels"]) == cleared + and summary["unresolved_channels"] == [] + and summary["anomalies"] == []) + + position, channel, virtual_us = (0.0, 0.0), 1, 0 + mismatches = 0 + for event in events: + request, response, path = event["request"], event["response"], event["path"] + distance, switches, action_s = 0.0, 0, 0 + if path in ("/measure", "/clear"): + target = (request["position"]["x"], request["position"]["y"]) + distance = math.dist(position, target) + position = target + if path == "/measure": + switches = int(channel != request["channel"]) + channel = request["channel"] + action_s = 5 + else: + action_s = 5 if response["clear_result"] == "success" else 3 + virtual_us += round(distance / 5 * 1_000_000) + (switches + action_s) * 1_000_000 + if round(response["virtual_time_s"] * 1_000_000) != virtual_us: + mismatches += 1 + checks["all_timestamps_match_microsecond_recomputation"] = mismatches == 0 + real_s = (events[-1]["response"]["real_timestamp_ms"] + - events[0]["response"]["real_timestamp_ms"]) / 1000 + checks["within_real_and_virtual_limits"] = ( + real_s < events[0]["response"]["remaining_real_duration_s"] + and virtual_us / 1_000_000 < events[0]["response"]["max_virtual_duration_s"]) + + report = { + "passed": all(checks.values()), + "source": "official_practice", + "strategy": summary["strategy"], + "problem": official["problem_no"], + "case_code": official["case_code"], + "official_jammer_count": official["jammer_count"], + "directional_jammer_count": official["directional_jammer_count"], + "cleared_channels": cleared, + "accepted_requests_including_enter_exit": len(events), + "measures": len([e for e in events if e["path"] == "/measure"]), + "clear_attempts": len(clears), + "clear_successes": len(successes), + "virtual_time_s": virtual_us / 1_000_000, + "real_elapsed_server_s": real_s, + "checks": checks, + "limits": "仅验证本官方演练案例；不触碰正式测试，不解读官方加密日志内容。", + } + save_json(folder / "verification.json", report) + manifest = {p.name: {"bytes": p.stat().st_size, + "sha256": hashlib.sha256(p.read_bytes()).hexdigest()} + for p in sorted(folder.iterdir()) if p.is_file() + and p.name != "manifest.json"} + save_json(folder / "manifest.json", manifest) + return report + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("folder", type=Path) + args = parser.parse_args() + report = audit(args.folder) + print(json.dumps({"passed": report["passed"], "case_code": report["case_code"], + "problem": report["problem"], "checks": report["checks"]}, + ensure_ascii=False, indent=2)) + raise SystemExit(0 if report["passed"] else 1) diff --git a/src/mcm26b/cli.py b/src/mcm26b/cli.py index 7b1d257..353588a 100644 --- a/src/mcm26b/cli.py +++ b/src/mcm26b/cli.py @@ -30,6 +30,7 @@ from .protocol.log import SessionLog from .protocol.rules import EXIT_USER Q3_STRATEGIES = {"grid": OmnidirectionalLoop, "seven": SevenBatch, "bayes": BayesLoop} +Q4_STRATEGIES = {"directional": DirectionalExtension} def _load_referee(): @@ -251,16 +252,33 @@ def _run(args) -> int: log = SessionLog(path=Path(args.log)) if args.log else SessionLog() client = SimulatorClient(args.robot_id, config=config, transport=transport, log=log) if args.problem == 4: - loop_cls = DirectionalExtension + strategy = args.q4_strategy + loop_cls = Q4_STRATEGIES.get(strategy) else: - loop_cls = Q3_STRATEGIES[args.strategy] + strategy = args.strategy + loop_cls = Q3_STRATEGIES.get(strategy) try: - summary = loop_cls(client, config).run() + 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 + 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() + else: + summary = loop_cls(client, config).run() + except RuntimeError as exc: + print(f"RL 策略不可用: {exc}", file=sys.stderr) + return 2 finally: if judge is not None: _settle_local_judge(judge) summary["robot_id"] = args.robot_id - summary["strategy"] = ("directional" if args.problem == 4 else args.strategy) + if args.problem == 4: + summary["strategy"] = "rl_q4" if strategy == "rl" else strategy + else: + summary["strategy"] = strategy if real_budget_s is not None: summary["self_test_real_budget_s"] = real_budget_s if args.summary_steps is not None and len(summary.get("steps", [])) > args.summary_steps: @@ -305,10 +323,19 @@ 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), default="bayes", + run.add_argument("--strategy", choices=tuple(Q3_STRATEGIES) + ("rl",), + 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；仅 --problem 3 生效") + "grid=49点网格基线，rl=PPO 强化学习选点（需先训练模型）；" + "仅 --problem 3 生效") + run.add_argument("--q4-strategy", choices=tuple(Q4_STRATEGIES) + ("rl",), + 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") + 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("--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/planner/rl_loop.py b/src/mcm26b/planner/rl_loop.py new file mode 100644 index 0000000..9a0348b --- /dev/null +++ b/src/mcm26b/planner/rl_loop.py @@ -0,0 +1,92 @@ +"""P6b 强化学习闭环接入：PPO 学习"下一检测点"选择（问题3/4）。 + +边界与保守性声明： +- RL 只替换测量点排序（在既有几何候选集内做 argmax），不生成新的 + 空间候选、不修改覆盖证明、不修改清除证书； +- 问题3：继承 :class:`BayesLoop`（七点覆盖 + 粒子滤波 + 滚动任务调度）， + 仅 :meth:`_select_point` 改由 PPO 输出候选编号； +- 问题4：继承 :class:`DirectionalExtension`（49 点定向覆盖 + 对跖/环探 + + 20m 有限圆盘覆盖后备清除），仅 :meth:`_next_probe` 改由 PPO 输出； + 遮挡计数、定向观测与后备清除兜底完全保留； +- 训练环境与运行特征同源（mcm26b.rl.features），模型加载时校验 + 观测/动作维度，不匹配即拒绝运行，避免静默错位。 + +默认模型位置：output/rl/q3_ppo.zip、output/rl/q4_ppo.zip。 +""" + +from __future__ import annotations + +from pathlib import Path + +from ..config import ProblemConfig, Vec2 +from ..rl.policy import PointSelectionPolicy, default_model_path +from . import DirectionalExtension +from .bayes_loop import BayesLoop + + +class RLBayesLoop(BayesLoop): + """问题3：贝叶斯闭环骨架 + PPO 定位选点（--strategy rl）。""" + + def __init__(self, client, config: ProblemConfig = ProblemConfig(), *, + model_path: str | Path | None = None, device: str = "auto"): + super().__init__(client, config) + self.rl_model_path = Path(model_path) if model_path else default_model_path(3) + self.rl_policy = PointSelectionPolicy(self.rl_model_path, device=device) + self.rl_probe_actions: list[dict] = [] + + def _select_point(self, channel: int) -> Vec2 | None: + region = self.belief.candidate_set(channel) + if region is None: + return None + point, action = self.rl_policy.select( + region, self.client.state.position, channel, self.config, + problem=3, blocked=[], belief=self.belief.channels[channel], + virtual_time_s=self.client.state.virtual_time_s) + self.rl_probe_actions.append({ + "channel": channel, "action": int(action), + "position": [round(point[0], 3), round(point[1], 3)], + "virtual_time_s": self.client.state.virtual_time_s, + }) + return point + + def _summary(self, info) -> dict: + report = super()._summary(info) + report["strategy"] = "rl" + report["rl_model"] = str(self.rl_model_path) + report["rl_device"] = self.rl_policy.device + report["rl_probe_actions"] = self.rl_probe_actions + return report + + +class RLDirectionalLoop(DirectionalExtension): + """问题4：定向闭环骨架 + PPO 遮挡梯子选点（--q4-strategy rl）。""" + + def __init__(self, client, config: ProblemConfig = ProblemConfig(), *, + model_path: str | Path | None = None, device: str = "auto"): + super().__init__(client, config) + self.rl_model_path = Path(model_path) if model_path else default_model_path(4) + self.rl_policy = PointSelectionPolicy(self.rl_model_path, device=device) + self.rl_probe_actions: list[dict] = [] + + def _next_probe(self, region, channel: int) -> Vec2: + blocked = self.blocked_probes.get(channel, []) + point, action = self.rl_policy.select( + region, self.client.state.position, channel, self.config, + problem=4, blocked=blocked, + belief=self.belief.channels[channel], + virtual_time_s=self.client.state.virtual_time_s) + self.rl_probe_actions.append({ + "channel": channel, "action": int(action), + "position": [round(point[0], 3), round(point[1], 3)], + "blocked_count": len(blocked), + "virtual_time_s": self.client.state.virtual_time_s, + }) + return point + + def _summary(self, info) -> dict: + report = super()._summary(info) + report["strategy"] = "rl_q4" + report["rl_model"] = str(self.rl_model_path) + report["rl_device"] = self.rl_policy.device + report["rl_probe_actions"] = self.rl_probe_actions + return report diff --git a/src/mcm26b/rl/__init__.py b/src/mcm26b/rl/__init__.py new file mode 100644 index 0000000..b5a1480 --- /dev/null +++ b/src/mcm26b/rl/__init__.py @@ -0,0 +1,14 @@ +"""问题3/4 强化学习扩展（可选依赖组 ``rl``）。 + +- features.py：观测/候选动作的纯几何接口（numpy），训练与运行共享； +- env.py：单源定位 Gymnasium 训练环境（依赖 referee，仅训练侧加载）； +- policy.py：PPO 模型加载与确定性推理（惰性加载 torch/sb3）。 + +运行闭环默认不导入 torch/stable-baselines3；只有 CLI 显式选择 +``--strategy rl``（问题3）或 ``--q4-strategy rl``（问题4）时， +``planner.rl_loop`` 才构造 :class:`policy.PointSelectionPolicy`。 +""" + +from .features import candidate_actions, build_observation + +__all__ = ["candidate_actions", "build_observation"] diff --git a/src/mcm26b/rl/_constants.py b/src/mcm26b/rl/_constants.py new file mode 100644 index 0000000..970a425 --- /dev/null +++ b/src/mcm26b/rl/_constants.py @@ -0,0 +1,11 @@ +"""RL 观测/动作空间常量：模型接口的唯一字面量来源。 + +布局说明见 features.py；这些数字由训练脚本写入模型接口， +改动其中任一值都会使已训练模型失配，须同步重训。 +""" + +GLOBAL_DIM = 41 +PER_ACTION_DIM = 6 +NUM_ACTIONS = 60 +LADDER_SLOTS = 8 +OBS_DIM = GLOBAL_DIM + NUM_ACTIONS * PER_ACTION_DIM # 401 diff --git a/src/mcm26b/rl/env.py b/src/mcm26b/rl/env.py new file mode 100644 index 0000000..6f81cc9 --- /dev/null +++ b/src/mcm26b/rl/env.py @@ -0,0 +1,308 @@ +"""问题3/4 的强化学习训练环境：单源"测向—定位—清除"子任务。 + +环境与自建评测机 referee 共享同一边界（内存回环，与真实 HTTP 同协议）， +观测只来自 :class:`mcm26b.belief.BeliefState` 与协议响应；真值仅在 reset +时用于布设课程场景，不进入观测。策略端看到的接口与闭环运行时一致。 + +问题抽象：机器狗已获得某频道首次 direction 读数，需要在一组几何可行候选 +检测点中选择下一个测点，目标是尽快把候选集最小包围圆压到 20m 清除证书 +阈值并清除成功。问题4额外引入定向源遮挡：保证接收盘内出现 no_signal 是 +"朝向遮挡"的观测证据，环境记录 blocked 序列并开放环探候选。 + +该环境是训练侧组件（依赖 referee + gymnasium），运行时代码不导入本模块； +导入依赖由可选组 ``[dependency-groups] rl`` 提供。 +""" + +from __future__ import annotations + +import math +from random import Random + +import gymnasium as gym +import numpy as np +from gymnasium import spaces + +from ..belief import BeliefState +from ..config import ProblemConfig +from ..planner import clear_certificate, fallback_clear_cells, safe_disk_radius +from ..protocol.client import SimulatorClient +from ..protocol.log import SessionLog +from ..protocol.rules import CLEAR_SUCCESS, DIRECTION, NEAR, NO_SIGNAL +from .features import NUM_ACTIONS, OBS_DIM, build_observation, candidate_actions + + +class LocalizationEnv(gym.Env): + """单源定位清除 Gymnasium 环境。 + + problem=3：全向源；problem=4：定向/全向混合（定向概率 0.5，训练分布 + 比题面更稠密以覆盖遮挡分支；正式策略仍由闭环的后备清除兜底）。 + """ + + metadata = {"render_modes": []} + + def __init__(self, config: ProblemConfig | None = None, *, + problem: int = 3, max_episode_steps: int = 16, + directional_probability: float = 0.5, + real_budget_s: float = 3600.0, + error_seed: int | None = None): + super().__init__() + if problem not in (3, 4): + raise ValueError("problem 必须是 3 或 4") + self.config = config or ProblemConfig() + self.problem = int(problem) + self.max_episode_steps = int(max_episode_steps) + self.directional_probability = float(directional_probability) + self.real_budget_s = float(real_budget_s) + self.error_seed = error_seed + self.observation_space = spaces.Box(low=-10.0, high=10.0, + shape=(OBS_DIM,), dtype=np.float32) + self.action_space = spaces.Discrete(NUM_ACTIONS) + + # 惰性导入：本模块只属于训练/评估侧，运行闭环不加载 referee。 + from referee import LocalSimulator as _RefereeLocalSimulator + from referee import Source + from ..harness import LocalSimTransport, referee_spec + self._Source = Source + self._LocalSimulator = _RefereeLocalSimulator + self._LocalSimTransport = LocalSimTransport + self._referee_spec = referee_spec + + self._scene_rng = Random() + self._simulator = None + self._client = None + self._source = None + self._belief: BeliefState | None = None + self._channel: int = 1 + self._blocked: list = [] + self._directional_observed = False + self._clear_failed = False + self._steps = 0 + self._terminated = False + self._truncated = False + self._measured_points: set = set() + self._fallback_attempts = 0 + + # ------------------------------------------------------------------ 场景 + def _make_source_and_probe(self): + """用真值布设一个"首次测向必为 direction"的课程场景（真值不进观测）。""" + rng = self._scene_rng + field = self.config.field + channel = rng.randint(field.channel_min, field.channel_max) + radius = field.omega_radius_m * math.sqrt(rng.random()) + angle = rng.uniform(0.0, 2 * math.pi) + x, y = radius * math.cos(angle), radius * math.sin(angle) + r_eff = rng.uniform(field.r_eff_min_m, field.r_eff_max_m) + directional = self.problem == 4 and rng.random() < self.directional_probability + heading = rng.uniform(0.0, 360.0) if directional else 0.0 + + # 首次检测点：距离 [300, min(1450, r_eff-20)] 内随机，定向源取朝向 + # ±80°（远离 90° 边界，保证 direction）。机器狗可到 Ω 外 [题面]。 + max_distance = max(310.0, min(1450.0, r_eff - 20.0)) + if max_distance < 310.0: + max_distance = 310.0 + probe_distance = rng.uniform(300.0, max_distance) + probe_bearing = (heading + rng.uniform(-80.0, 80.0) + if directional else rng.uniform(0.0, 360.0)) + probe_radians = math.radians(probe_bearing) + probe = (x + probe_distance * math.cos(probe_radians), + y + probe_distance * math.sin(probe_radians)) + source = self._Source(channel=channel, x=x, y=y, r_eff_m=r_eff, + directional=directional, heading_deg=heading) + return source, probe + + def _make_episode(self) -> bool: + """重建模拟器并完成首次测向；返回 False 表示连续未得到 direction。""" + for _ in range(20): + self._source, probe = self._make_source_and_probe() + self._simulator = self._LocalSimulator( + [self._source], robot_id="rl-train", + spec=self._referee_spec( + self.config, max_real_duration_s=self.real_budget_s), + 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( + "rl-train", config=self.config, + transport=self._LocalSimTransport(self._simulator), + log=SessionLog()) + self._client.enter() + outcome = self._client.measure(probe, self._source.channel) + if outcome.result == DIRECTION: + self._channel = self._source.channel + self._belief = BeliefState(self.config) + self._belief.update_measure(self._channel, probe, outcome.result, + outcome.svd_deg) + self._blocked = [] + self._directional_observed = False + self._clear_failed = False + self._measured_points = {self._point_key(probe)} + self._fallback_attempts = 0 + return True + # near / no_signal 首测属于概率为零的边界或课程构造失败，换场景。 + continue + return False + + # ------------------------------------------------------------------ Gym API + @staticmethod + def _point_key(point) -> tuple[int, int]: + return (int(round(point[0] * 2)), int(round(point[1] * 2))) + + 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 + if not self._make_episode(): + raise RuntimeError("连续 20 次无法构造 direction 首测场景") + observation = self._observe() + return observation, self._info() + + def _observe(self) -> np.ndarray: + region = self._belief.candidate_set(self._channel) + if region is None: + raise RuntimeError("首次测向后信念状态缺失候选集") + return build_observation( + region, self._client.state.position, self._channel, self.config, + self.problem, self._blocked, self._belief, + virtual_time_s=self._client.state.virtual_time_s) + + def _info(self) -> dict: + source = self._source + return { + "channel": self._channel, + "source": {"x": source.x, "y": source.y, "r_eff_m": source.r_eff_m, + "directional": source.directional, + "heading_deg": source.heading_deg}, + "steps": self._steps, + "blocked_count": len(self._blocked), + "directional_observed": self._directional_observed, + "fallback_attempts": self._fallback_attempts, + } + + def step(self, action): + if self._terminated or self._truncated: + raise RuntimeError("step() 需要在 terminated/truncated 后先 reset()") + if not 0 <= int(action) < NUM_ACTIONS: + raise ValueError(f"动作 {action} 超出 Discrete({NUM_ACTIONS})") + action = int(action) + self._steps += 1 + region = self._belief.candidate_set(self._channel) + if region is None: + return self._finish(observation=self._observe(), reward=-20.0, + terminated=True, truncated=False) + + # 达到清除证书且上次清除失败已被新测量重置：先自动清除（闭环语义）。 + cert = clear_certificate(region, self._channel, self.config) + if cert.certified and not self._clear_failed: + return self._try_clear(cert, observation=self._observe()) + + old_virtual = self._client.state.virtual_time_s + old_radius = region.mec()[1] + candidates = candidate_actions(region, self._client.state.position, + self.config, self._blocked) + point = candidates[action] + try: + outcome = self._client.measure(point, self._channel) + self._belief.update_measure(self._channel, point, outcome.result, + outcome.svd_deg) + except Exception: + return self._finish(observation=self._observe(), reward=-20.0, + terminated=True, truncated=False) + + cost_s = self._client.state.virtual_time_s - old_virtual + reward = -cost_s / 100.0 + if self._point_key(point) in self._measured_points: + reward -= 0.75 # 重复测量浪费一次检测+移动，抑制策略回路 + else: + self._measured_points.add(self._point_key(point)) + new_region = self._belief.candidate_set(self._channel) + new_radius = new_region.mec()[1] if new_region is not None else 0.0 + if outcome.result == DIRECTION: + # 密集成型奖励：一次测量把包围半径缩得越多越好。 + shrink = max(0.0, old_radius - new_radius) + reward += 3.0 * shrink / max(old_radius, 1.0) + elif outcome.result == NEAR: + reward += 1.5 + elif outcome.result == NO_SIGNAL: + center = region.mec()[0] + safe = safe_disk_radius(region, self.config) + if safe >= 0 and math.dist(point, center) <= safe + 1e-6: + if not self._directional_observed: + self._directional_observed = True + reward += 2.0 # 首次获得"朝向遮挡"证据（问题4关键分支） + self._blocked.append(point) + + self._clear_failed = False + cert = clear_certificate(new_region, self._channel, self.config) \ + if new_region is not None else None + observation = self._observe() + if cert is not None and cert.certified: + return self._try_clear(cert, observation=observation, + reward=reward) + if self.problem == 4 and len(self._blocked) >= self.config.search.max_blocked_probes: + return self._fallback_clear(observation=observation, reward=reward) + if self._steps >= self.max_episode_steps: + return self._finish(observation=observation, reward=reward - 2.0, + terminated=False, truncated=True) + return observation, reward, False, False, self._info() + + def _fallback_clear(self, *, observation, reward: float): + """问题4后备清除语义：遮挡梯子用尽后按 20m 网格覆盖逐格 /clear。 + + 与 DirectionalExtension._fallback_clear 同口径；训练环境借此让策略 + 学到"梯子耗尽的代价"，运行闭环仍由原后备清除函数执行。 + """ + region = self._belief.candidate_set(self._channel) + cells = fallback_clear_cells(region.polygon, self.config) + self._fallback_attempts = len(cells) + if len(cells) > self.config.search.fallback_max_cells: + return self._finish(observation=observation, reward=reward - 3.0, + terminated=False, truncated=True) + attempts = 0 + while cells: + cell = min(cells, key=lambda c: ( + math.dist(self._client.state.position, c), c)) + cells.remove(cell) + attempts += 1 + old_virtual = self._client.state.virtual_time_s + outcome = self._client.clear(cell, self._channel) + reward += -(self._client.state.virtual_time_s - old_virtual) / 100.0 + if outcome.result == CLEAR_SUCCESS: + self._belief.mark_cleared(self._channel) + reward += 8.0 + self._fallback_attempts = attempts + return self._finish(observation=observation, reward=reward, + terminated=True, truncated=False) + self._fallback_attempts = attempts + return self._finish(observation=observation, reward=reward - 20.0, + terminated=True, truncated=False) + + def _try_clear(self, cert, *, observation, reward: float = 0.0): + old_virtual = self._client.state.virtual_time_s + outcome = self._client.clear(cert.center, self._channel) + cost_s = self._client.state.virtual_time_s - old_virtual + reward += -cost_s / 100.0 + if outcome.result == CLEAR_SUCCESS: + self._belief.mark_cleared(self._channel) + reward += 10.0 + 0.25 * max(0, self.max_episode_steps - self._steps) + return self._finish(observation=observation, reward=reward, + terminated=True, truncated=False) + self._clear_failed = True + reward -= 1.0 + if self._steps >= self.max_episode_steps: + return self._finish(observation=observation, reward=reward, + terminated=False, truncated=True) + return observation, reward, False, False, self._info() + + def _finish(self, *, observation, reward, terminated, truncated): + self._terminated = bool(terminated) + self._truncated = bool(truncated) + info = self._info() + info["cleared"] = (self._belief.channels[self._channel].status.value + == "cleared") + return observation, float(reward), self._terminated, self._truncated, info + + def close(self): + return None diff --git a/src/mcm26b/rl/features.py b/src/mcm26b/rl/features.py new file mode 100644 index 0000000..c7de513 --- /dev/null +++ b/src/mcm26b/rl/features.py @@ -0,0 +1,173 @@ +"""RL 状态特征与候选动作生成（问题3/4 共用，运行时与训练环境同源）。 + +设计目标：把"下一检测点去哪"这一连续决策转化为固定顺序的离散动作， +让 PPO 策略在 GPU 上学习，同时不触碰闭环的保守性证明。候选集合始终由 +既有几何规则生成（保证接收盘、对称交会、定向遮挡梯子），RL 只负责在 +这些物理可行候选中排序；证书、覆盖证明与后备清除仍由 planner 层负责。 + +观测维度与候选编号一旦训练即成为模型接口，不得随意重排： +- 全局特征 41 维（含频道 one-hot 20 维）； +- 每个候选 6 维（相对位移、移动耗时、到 MEC 圆心距离、方位、保证接收标记）， + 共 60 × 6 = 360 维； +- 总观测 401 维。 +""" + +from __future__ import annotations + +import math + +import numpy as np + +from ..config import ProblemConfig, Vec2 +from ..planner import candidate_points, safe_disk_radius +from ._constants import GLOBAL_DIM, LADDER_SLOTS, NUM_ACTIONS, OBS_DIM, PER_ACTION_DIM + +__all__ = [ + "GLOBAL_DIM", + "LADDER_SLOTS", + "NUM_ACTIONS", + "OBS_DIM", + "PER_ACTION_DIM", + "candidate_actions", + "build_observation", + "empty_observation", +] + + +# 候选编号布局（训练/推理共享）： +# 0 保证接收盘圆心（或宽候选集 MEC 圆心） +# 1..36 保证接收盘 3 环 × 12 方向 +# 37..51 相对"机器人->MEC 圆心"来向的对称交会候选 3 环 × 5 偏角 +# 52..59 定向遮挡环探梯子 8 个方向（问题3也保留，作为宽环探索） +_RING_RATIOS = (0.4, 0.75, 1.0) +_RING_ANGLES = 12 +_GEO_OFFSETS = (-math.pi / 3, -math.pi / 6, 0.0, math.pi / 6, math.pi / 3) + + +def _probe_radius(region_radius_m: float, safe_m: float) -> float: + """环候选的基准半径：有保证接收盘用盘半径，否则退化为 MEC 半径。""" + if safe_m >= 0.0: + return safe_m + return max(region_radius_m, 10.0) + + +def candidate_actions(region, position: Vec2, config: ProblemConfig, + blocked: list[Vec2] | None = None) -> list[Vec2]: + """生成固定顺序的 60 个候选检测点。 + + 参数 region 为 belief.CandidateRegion（任意凸候选集）；blocked 是 + 定向遮挡记录（问题4），问题3 传空列表即可。返回顺序与模型训练时一致。 + """ + center, radius = region.mec() + safe = safe_disk_radius(region, config) + base_radius = _probe_radius(radius, safe) + points = candidate_points(tuple(center), base_radius, + _RING_RATIOS, _RING_ANGLES) + assert len(points) == 37 + + base_angle = math.atan2(position[1] - center[1], + position[0] - center[0]) + for fraction in _RING_RATIOS: + for offset in _GEO_OFFSETS: + angle = base_angle + offset + points.append((center[0] + base_radius * fraction * math.cos(angle), + center[1] + base_radius * fraction * math.sin(angle))) + + blocked = list(blocked or []) + first = blocked[0] if blocked else None + anchor = 0.0 + if first is not None: + anchor = math.atan2(first[1] - center[1], first[0] - center[0]) + ladder_radius = max(radius, 20.0) + for slot in range(LADDER_SLOTS): + angle = anchor + slot * math.pi / 4 + points.append((center[0] + ladder_radius * math.cos(angle), + center[1] + ladder_radius * math.sin(angle))) + assert len(points) == NUM_ACTIONS + return points + + +def _last_measurement_features(belief) -> tuple[tuple[float, float, float], + tuple[float, float]]: + """末次读数的 one-hot + 方向角 sin/cos（无读数取零）。""" + readings = getattr(belief, "measurements", None) or [] + if not readings: + return (1.0, 0.0, 0.0), (0.0, 0.0) + last = readings[-1] + result = last.get("result") + no_signal = 1.0 if result == "no_signal" else 0.0 + direction = 1.0 if result == "direction" else 0.0 + near = 1.0 if result == "near" else 0.0 + svd = last.get("svd_deg") + if direction and svd is not None: + radians = math.radians(float(svd)) + return (no_signal, direction, near), (math.sin(radians), math.cos(radians)) + return (no_signal, direction, near), (0.0, 0.0) + + +def build_observation(region, position: Vec2, channel: int, config: ProblemConfig, + problem: int, blocked: list[Vec2] | None = None, + belief=None, virtual_time_s: float = 0.0) -> np.ndarray: + """构造 PPO 观测向量（shape=(401,)，与训练环境逐位一致）。""" + field, search = config.field, config.search + center, radius = region.mec() + safe = safe_disk_radius(region, config) + readings, svd_pair = _last_measurement_features(belief) + xs = [p[0] for p in region.polygon] + ys = [p[1] for p in region.polygon] + + global_features = [ + position[0] / 2000.0, + position[1] / 2000.0, + (channel - field.channel_min) / max(field.channel_max - field.channel_min, 1), + center[0] / 2000.0, + center[1] / 2000.0, + radius / 2000.0, + max(-1.0, min(1.0, safe / 1000.0)), + min(len(getattr(belief, "measurements", []) if belief is not None else []), + 12) / 12.0, + min(float(virtual_time_s or 0.0), field.max_virtual_duration_s) + / field.max_virtual_duration_s, + # 证书信息由运行时的 clear_certificate 判定传入；训练环境用 region.mec + # 的保守近似：radius + slack <= 20 - margin 即可清除。 + 1.0 if radius + region.slack_m <= field.clear_radius_m - search.clear_margin_m else 0.0, + float(problem == 4), + min(len(blocked or []) / max(search.max_blocked_probes, 1), 1.0), + (blocked[0][0] / 2000.0) if blocked else 0.0, + (blocked[0][1] / 2000.0) if blocked else 0.0, + readings[0], + readings[1], + readings[2], + svd_pair[0], + svd_pair[1], + (max(xs) - min(xs)) / 4000.0, + (max(ys) - min(ys)) / 4000.0, + ] + one_hot = np.zeros(field.channel_max - field.channel_min + 1, dtype=np.float32) + one_hot[int(channel) - field.channel_min] = 1.0 + global_features.extend(one_hot.tolist()) + assert len(global_features) == GLOBAL_DIM + + action_features: list[float] = [] + for point in candidate_actions(region, position, config, blocked): + dx, dy = point[0] - position[0], point[1] - position[1] + distance = math.hypot(dx, dy) + action_features.extend([ + dx / 2000.0, + dy / 2000.0, + min(distance / (config.cost.speed_mps * 1500.0), 2.0), # 移动耗时 / 1500s + math.hypot(point[0] - center[0], point[1] - center[1]) / 2000.0, + (math.degrees(math.atan2(dy, dx)) % 360.0) / 360.0, + 1.0 if safe >= 0 and math.hypot(point[0] - center[0], + point[1] - center[1]) <= safe + 1e-6 else 0.0, + ]) + assert len(action_features) == NUM_ACTIONS * PER_ACTION_DIM + + observation = np.asarray(global_features + action_features, dtype=np.float32) + assert observation.shape == (OBS_DIM,) + return observation + + +def empty_observation() -> np.ndarray: + """依赖缺失时的零观测（仅用于错误提示路径，不进入训练数据）。""" + return np.zeros(OBS_DIM, dtype=np.float32) diff --git a/src/mcm26b/rl/policy.py b/src/mcm26b/rl/policy.py new file mode 100644 index 0000000..8172016 --- /dev/null +++ b/src/mcm26b/rl/policy.py @@ -0,0 +1,71 @@ +"""RL 选点策略加载与推理（运行时接入 planner 的唯一入口）。 + +运行闭环对依赖保持惰性：只有显式选择 ``--strategy rl`` / ``--q4-strategy rl`` +时才导入 stable_baselines3/torch。模型文件由 scripts/train_rl.py 生成， +观测/动作布局与训练环境共享 features 模块。 +""" + +from __future__ import annotations + +from pathlib import Path + +from ..config import ProblemConfig, Vec2 +from ._constants import NUM_ACTIONS, OBS_DIM +from .features import build_observation, candidate_actions + + +class PointSelectionPolicy: + """加载 PPO 模型，把当前候选集映射到 60 个候选检测点之一。""" + + def __init__(self, model_path: str | Path, *, device: str = "auto"): + path = Path(model_path) + if not path.is_file(): + raise RuntimeError( + f"未找到 RL 模型 {path}。请先安装 rl 依赖组并训练：\n" + f" uv sync --group rl --group dev\n" + f" uv run python scripts/train_rl.py --problem 3 --timesteps 120000\n" + f" uv run python scripts/train_rl.py --problem 4 --timesteps 40000") + try: + from stable_baselines3 import PPO + except ImportError as exc: + raise RuntimeError( + "缺少 RL 运行依赖（stable-baselines3）。请执行 " + "`uv sync --group rl` 后重试。") from exc + if device == "auto": + try: + import torch + device = "cuda" if torch.cuda.is_available() else "cpu" + except ImportError: + device = "cpu" + self.device = device + self.model = PPO.load(str(path), device=device) + try: + obs_dim = self.model.observation_space.shape[0] + action_dim = self.model.action_space.n + except Exception as exc: + raise RuntimeError(f"模型空间与 RL 接口不兼容: {path}") from exc + if obs_dim != OBS_DIM or action_dim != NUM_ACTIONS: + raise RuntimeError( + f"模型接口失配: 期望 obs={OBS_DIM}/act={NUM_ACTIONS}，" + f"实际 obs={obs_dim}/act={action_dim}。请用当前版本重新训练。") + self.path = path + + def select(self, region, position: Vec2, channel: int, + config: ProblemConfig, problem: int, + blocked: list[Vec2] | None = None, belief=None, + virtual_time_s: float = 0.0) -> tuple[Vec2, int]: + """返回 (检测点, 候选编号)；确定性 argmax（评估/比赛口径）。""" + observation = build_observation( + region, position, channel, config, problem, blocked, belief, + virtual_time_s=virtual_time_s) + action, _state = self.model.predict(observation, deterministic=True) + action = int(action) + candidates = candidate_actions(region, position, config, blocked) + return candidates[action], action + + +def default_model_path(problem: int) -> Path: + """各问题的默认模型位置（训练脚本输出口径）。""" + if problem not in (3, 4): + raise ValueError("problem 必须是 3 或 4") + return Path("output/rl") / f"q{problem}_ppo.zip" diff --git a/tests/test_planner_revision.py b/tests/test_planner_revision.py index 9c556a4..54b95bc 100644 --- a/tests/test_planner_revision.py +++ b/tests/test_planner_revision.py @@ -5,12 +5,13 @@ import math import pytest from mcm26b.config import ProblemConfig +from mcm26b.harness import LocalSimTransport, LocalSimulator from mcm26b.planner import ClearCertificate, DirectionalExtension from mcm26b.planner.bayes_loop import BayesLoop, Task from mcm26b.planner.seven import validate_seven_coverage from mcm26b.protocol.client import SimulatorClient from mcm26b.protocol.rules import DIRECTION, NO_SIGNAL -from mcm26b.sim import LocalSimulator, LocalSimTransport, Source +from referee import Source def setup(sources=(), cls=BayesLoop, config=ProblemConfig()): diff --git a/tests/test_rl.py b/tests/test_rl.py new file mode 100644 index 0000000..bd98045 --- /dev/null +++ b/tests/test_rl.py @@ -0,0 +1,102 @@ +"""RL 扩展验收：特征/动作布局、训练环境闭环与模型接口守卫。 + +RL 依赖属于可选组 [dependency-groups] rl；未安装 numpy/gymnasium 时 +全部跳过（运行闭环不依赖它们）。 +""" + +import math +import unittest +from pathlib import Path + +pytest = None +try: + import pytest + numpy = pytest.importorskip("numpy") + pytest.importorskip("gymnasium") +except Exception: # pragma: no cover - 无 RL 依赖环境 + numpy = None + +if numpy is not None: + from mcm26b.belief import CandidateRegion + from mcm26b.config import ProblemConfig + from mcm26b.rl._constants import GLOBAL_DIM, NUM_ACTIONS, OBS_DIM, PER_ACTION_DIM + from mcm26b.rl.env import LocalizationEnv + from mcm26b.rl.features import build_observation, candidate_actions + from mcm26b.rl.policy import default_model_path + +CONFIG = ProblemConfig() +STATION = (0.0, 0.0) +BEARING = 30.0 + + +def _region(): + region = CandidateRegion.initial(STATION, CONFIG) + region.clip_wedge(STATION, BEARING, 1.005) + return region + + +@unittest.skipIf(numpy is None, "RL 依赖组未安装") +class FeatureLayoutTests(unittest.TestCase): + def test_action_candidates_have_stable_count_and_order(self): + region = _region() + first = candidate_actions(region, STATION, CONFIG, []) + self.assertEqual(len(first), NUM_ACTIONS) + second = candidate_actions(region, STATION, CONFIG, []) + self.assertEqual(first, second) + self.assertEqual(first[0], region.mec()[0]) # 0 号候选 = MEC 圆心 + + def test_observation_dimension_matches_model_contract(self): + region = _region() + obs = build_observation(region, STATION, 3, CONFIG, problem=3, + blocked=[], virtual_time_s=0.0) + self.assertEqual(obs.shape, (OBS_DIM,)) + self.assertEqual(OBS_DIM, GLOBAL_DIM + NUM_ACTIONS * PER_ACTION_DIM) + + def test_blocked_changes_observation_but_not_layout(self): + region = _region() + clean = build_observation(region, STATION, 3, CONFIG, problem=4, + blocked=[], virtual_time_s=0.0) + blocked = build_observation(region, STATION, 3, CONFIG, problem=4, + blocked=[(100.0, 0.0)], virtual_time_s=0.0) + self.assertEqual(clean.shape, blocked.shape) + self.assertFalse(numpy.allclose(clean, blocked)) + + +@unittest.skipIf(numpy is None, "RL 依赖组未安装") +class TrainingEnvironmentTests(unittest.TestCase): + def test_q3_episode_reset_and_step_shapes(self): + env = LocalizationEnv(problem=3) + obs, info = env.reset(seed=1) + self.assertEqual(obs.shape, (OBS_DIM,)) + self.assertIn("source", info) + obs, reward, terminated, truncated, info = env.step(0) + self.assertEqual(obs.shape, (OBS_DIM,)) + self.assertIsInstance(reward, float) + self.assertFalse(terminated and truncated) + + def test_q4_directional_episode_reports_blocked_or_fallback(self): + env = LocalizationEnv(problem=4, directional_probability=1.0) + obs, info = env.reset(seed=7) + self.assertTrue(info["source"]["directional"]) + terminated = truncated = False + for _ in range(env.max_episode_steps + 2): + obs, reward, terminated, truncated, info = env.step( + env.action_space.sample()) + if terminated or truncated: + break + self.assertTrue(terminated or truncated) + # 定向分支：要么清除，要么走过后备清除/截断语义。 + self.assertIn("fallback_attempts", info) + + def test_missing_model_raises_clear_error(self): + from mcm26b.rl.policy import PointSelectionPolicy + with self.assertRaises(RuntimeError): + PointSelectionPolicy("output/rl/does-not-exist.zip") + + def test_default_model_paths(self): + self.assertEqual(default_model_path(3), Path("output/rl/q3_ppo.zip")) + self.assertEqual(default_model_path(4), Path("output/rl/q4_ppo.zip")) + + +if __name__ == "__main__": + unittest.main() diff --git a/uv.lock b/uv.lock index 5ea3789..c33a485 100644 --- a/uv.lock +++ b/uv.lock @@ -16,406 +16,562 @@ members = [ [[package]] name = "alembic" version = "1.19.2" -source = { registry = "https://mirrors.aliyun.com/pypi/simple/" } +source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "mako" }, { name = "sqlalchemy" }, { name = "tomli", marker = "python_full_version < '3.11'" }, { name = "typing-extensions" }, ] -sdist = { url = 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