diff --git a/.gitignore b/.gitignore index 38c13d1..32b8b20 100644 --- a/.gitignore +++ b/.gitignore @@ -20,6 +20,7 @@ env/ # Logs / test / tooling *.log +.cache/ .pytest_cache/ .mypy_cache/ .ruff_cache/ diff --git a/mario-rl-mvp/README.md b/mario-rl-mvp/README.md index 9b4e674..e6cc17f 100644 --- a/mario-rl-mvp/README.md +++ b/mario-rl-mvp/README.md @@ -11,6 +11,7 @@ mario-rl-mvp/ train_ppo.py eval.py record_video.py + plot_model_max_x_trend.py utils.py artifacts/ models/ @@ -32,6 +33,44 @@ python -m pip install --upgrade pip setuptools wheel pip install -r requirements.txt ``` +## 2.1 环境准备（WSL / Ubuntu） + +如果系统 Python 缺少 `venv/pip`，推荐直接用 `uv` 创建环境并安装依赖： + +```bash +cd /home/roog/super-mario/mario-rl-mvp +uv venv .venv -p /usr/bin/python3.10 +uv pip install --python .venv/bin/python -r requirements.txt +``` + +如果你更倾向用系统 `venv`，先安装： + +```bash +sudo apt-get update +sudo apt-get install -y python3.10-venv python3-pip +``` + +### RTX 50 系列（如 RTX 5080）GPU 说明 + +如果你看到类似： + +```text +... CUDA capability sm_120 is not compatible with the current PyTorch installation ... +``` + +说明当前 torch wheel 不包含 `sm_120` 内核。可直接升级到 `cu128` nightly： + +```bash +cd /home/roog/super-mario/mario-rl-mvp +uv pip install --python .venv/bin/python --upgrade --pre torch --index-url https://download.pytorch.org/whl/nightly/cu128 +``` + +验证 GPU： + +```bash +.venv/bin/python -c "import torch; print(torch.__version__); print(torch.version.cuda); print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'N/A'); print(torch.cuda.get_device_capability(0) if torch.cuda.is_available() else 'N/A')" +``` + 可选系统依赖（用于 ffmpeg 转码与潜在 SDL 兼容）： ```bash @@ -40,12 +79,19 @@ brew install ffmpeg sdl2 ## 3. 一条命令开始训练 -默认 CPU 训练（如果检测到可用且稳定的 MPS，会自动尝试启用，否则自动回退 CPU）： +默认 `--device auto` 训练（优先 CUDA，其次 MPS，最后 CPU）： ```bash python -m src.train_ppo ``` +显式指定 `--device cuda` 或 `--device mps` 时，如果该设备不可用，脚本会默认报错（避免静默回退到 CPU）。 +若你明确接受回退，可加： + +```bash +python -m src.train_ppo --device cuda --allow-device-fallback +``` + 常用覆盖参数： ```bash @@ -78,6 +124,30 @@ python -m src.train_ppo \ --total-timesteps 300000 ``` +我目前的参数 + +``` +python -m src.train_ppo \ + --init-model-path artifacts/models/latest_model.zip \ + --n-envs 16 \ + --allow-partial-init \ + --reward-mode progress \ + --movement simple \ + --ent-coef 0.001 \ + --learning-rate 2e-5 \ + --n-steps 2048 \ + --gamma 0.99 \ + --death-penalty -50 \ + --stall-penalty 0.05 \ + --stall-steps 40 \ + --backward-penalty-scale 0.01 \ + --milestone-bonus 2.0 \ + --no-progress-terminate-steps 300 \ + --no-progress-terminate-penalty 10 \ + --time-penalty -0.01 \ + --total-timesteps 1200000 +``` + ### 3.1 从已有模型继续训练（`--init-model-path`） - 用途：加载已有 `.zip` 权重后继续训练，适合“不中断实验目标但调整探索参数”。 @@ -143,13 +213,35 @@ tensorboard --logdir artifacts/logs --port 6006 加载最新模型，跑 N 个 episode，输出平均指标： ```bash -python -m src.eval --episodes 5 --stochastic +python -m src.eval \ + --model-path artifacts/models/latest_model.zip \ + --episodes 20 \ + --movement simple \ + --reward-mode progress \ + --no-progress-terminate-steps 300 \ + --no-progress-terminate-penalty 10 \ + --death-penalty -50 \ + --stall-penalty 0.05 \ + --stall-steps 40 \ + --time-penalty -0.01 \ + --epsilon 0.08 ``` 可指定模型： ```bash -python -m src.eval --model-path artifacts/models/latest_model.zip --episodes 10 --stochastic +python -m src.eval \ + --model-path artifacts/models/latest_model.zip \ + --episodes 20 \ + --movement simple \ + --reward-mode progress \ + --no-progress-terminate-steps 300 \ + --no-progress-terminate-penalty 10 \ + --death-penalty -50 \ + --stall-penalty 0.05 \ + --stall-steps 40 \ + --time-penalty -0.01 \ + --stochastic ``` 注意：`eval.py` 默认 `--movement auto`，会按模型动作维度自动匹配 `right_only/simple`，避免动作空间不一致导致 `KeyError`。 @@ -174,13 +266,41 @@ _total_timesteps = 150000 默认录制约 10 秒 mp4 到 `artifacts/videos/`： ```bash -python -m src.record_video --duration-sec 10 --fps 30 --stochastic +python -m src.record_video \ + --model-path artifacts/models/latest_model.zip \ + --movement simple \ + --reward-mode progress \ + --no-progress-terminate-steps 300 \ + --no-progress-terminate-penalty 10 \ + --death-penalty -50 \ + --stall-penalty 0.05 \ + --stall-steps 40 \ + --time-penalty -0.01 \ + --epsilon 0.08 \ + --duration-sec 30 ``` -可指定输出路径： +或者稳定版本 ```bash -python -m src.record_video --output artifacts/videos/demo.mp4 --stochastic --duration-sec 10 +python -m src.record_video \ + --model-path artifacts/models/latest_model.zip \ + --movement simple \ + --reward-mode progress \ + --no-progress-terminate-steps 300 \ + --no-progress-terminate-penalty 10 \ + --death-penalty -50 \ + --stall-penalty 0.05 \ + --stall-steps 40 \ + --time-penalty -0.01 \ + --epsilon 0.08 \ + --epsilon-random-mode uniform \ + --max-steps 6000 +``` +可选: + +```bash +--output artifacts/videos/mario_eps008.mp4 ``` 注意：`record_video.py` 默认 `--movement auto`，会按模型自动匹配动作空间。 @@ -190,6 +310,47 @@ python -m src.record_video --output artifacts/videos/demo.mp4 --stochastic --dur - 默认通过 `imageio + ffmpeg` 输出 mp4 - 若 mp4 写入失败，会自动降级保存帧序列（PNG），并打印 ffmpeg 转码命令 +## 5.1 模型趋势可视化（HTML / Markdown） + +用于可视化 `artifacts/models/` 里的模型在训练过程中的关键指标趋势，输出中文 HTML 或 Markdown 报告。 + +默认命令： + +```bash +python -m src.plot_model_max_x_trend +``` + +默认输出： + +- `artifacts/reports/model_max_x_trend.html` + +输出 Markdown 报告： + +```bash +python -m src.plot_model_max_x_trend --format markdown +``` + +Markdown 默认输出： + +- `artifacts/reports/model_max_x_trend.md` + +可选参数（自定义目录/输出）： + +```bash +uv run python -m src.plot_model_max_x_trend \ + --models-dir artifacts/models \ + --logs-dir artifacts/logs \ + --format markdown \ + --output artifacts/reports/model_max_x_trend.md +``` + +报告内容： + +- 主趋势：`max_x`（最大前进距离） +- 多维趋势：平均回报、平均回合步数、通关率、无进展终止率、死亡终止率、超时终止率、硬卡死终止率 +- 模型明细表：每个 checkpoint/final 模型对应的指标值、匹配步数、来源 TensorBoard tag +- 术语解释：Run、Checkpoint、model_step、matched_step、TensorBoard Tag 等专有名词 + ## 6. 动作空间选择说明 默认 `RIGHT_ONLY`，原因： @@ -231,22 +392,23 @@ python -m src.train_ppo --reward-mode clip ```bash python -m src.train_ppo \ - --init-model-path artifacts/models/latest_model.zip \ + --init-model-path /home/roog/super-mario/mario-rl-mvp/artifacts/models/ppo_SuperMarioBros-1-1-v0_20260212_205220/ppo_mario_ckpt_100000_steps.zip \ + --n-envs 16 \ --allow-partial-init \ --reward-mode progress \ --movement simple \ - --ent-coef 0.04 \ + --ent-coef 0.01 \ --learning-rate 1e-4 \ - --n-steps 512 \ - --gamma 0.995 \ - --death-penalty -120 \ - --stall-penalty 0.2 \ - --stall-steps 20 \ - --backward-penalty-scale 0.03 \ + --n-steps 1024 \ + --gamma 0.99 \ + --death-penalty -50 \ + --stall-penalty 0.05 \ + --stall-steps 40 \ + --backward-penalty-scale 0.01 \ --milestone-bonus 2.0 \ - --no-progress-terminate-steps 80 \ - --no-progress-terminate-penalty 30 \ - --total-timesteps 150000 + --no-progress-terminate-steps 300 \ + --no-progress-terminate-penalty 10 \ + --total-timesteps 300000 ``` ## 8. 常见问题排查 @@ -312,6 +474,30 @@ python -m src.train_ppo --init-model-path artifacts/models/latest_model.zip --mo 3) 或者直接不加载旧模型，从头训练新动作空间。 +### 8.6 `cudaGetDeviceCount ... Error 304`（WSL 下 CUDA 初始化失败） + +如果训练启动时看到： + +```text +[device] cpu | CUDA unavailable, using CPU. +[device_diag] ... torch.cuda.is_available()=False ... Error 304 ... +``` + +说明不是 `device` 参数没传，而是 CUDA 运行时在当前环境初始化失败。 + +先做两步确认： + +```bash +nvidia-smi --query-gpu=name,driver_version,compute_cap --format=csv,noheader +.venv/bin/python -c "import torch; print(torch.__version__); print(torch.version.cuda); print(torch.cuda.is_available())" +``` + +常见原因是 WSL GPU 栈/驱动状态异常，而不是 PPO 代码本身。若你是临时跑通实验，可先显式 CPU： + +```bash +python -m src.train_ppo --device cpu +``` + ## 9. 最小 smoke test（按顺序执行） ```bash diff --git a/mario-rl-mvp/requirements.txt b/mario-rl-mvp/requirements.txt index a747e8f..34cf6ba 100644 --- a/mario-rl-mvp/requirements.txt +++ b/mario-rl-mvp/requirements.txt @@ -1,4 +1,5 @@ -torch==2.5.1 +# Keep torch unpinned to avoid forcing old wheels on new GPUs (e.g. RTX 50xx). +torch>=2.5.1 stable-baselines3==2.3.2 gym==0.26.2 gymnasium==0.29.1 diff --git a/mario-rl-mvp/src/env.py b/mario-rl-mvp/src/env.py index d82b6d6..6983d92 100644 --- a/mario-rl-mvp/src/env.py +++ b/mario-rl-mvp/src/env.py @@ -212,6 +212,7 @@ class ProgressRewardEnv(gym.Wrapper): truncated = True shaped_reward -= self.no_progress_terminate_penalty info["terminated_by_stall"] = True + info["done_reason"] = "no_progress" if terminated or truncated: if bool(info.get("flag_get", False)): @@ -222,6 +223,71 @@ class ProgressRewardEnv(gym.Wrapper): return obs, shaped_reward, terminated, truncated, info +class TimePenaltyHardStuckEnv(gym.Wrapper): + """Optional living cost and hard-stuck truncation based on x_pos movement.""" + + def __init__( + self, + env: gym.Env, + time_penalty: float = 0.0, + hard_stuck_steps: int = 0, + hard_stuck_epsilon: float = 1.0, + hard_stuck_penalty: float = 5.0, + ): + super().__init__(env) + if time_penalty > 0.0: + raise ValueError(f"time_penalty must be <= 0.0, got {time_penalty}") + if hard_stuck_steps < 0: + raise ValueError(f"hard_stuck_steps must be >= 0, got {hard_stuck_steps}") + if hard_stuck_epsilon < 0.0: + raise ValueError(f"hard_stuck_epsilon must be >= 0.0, got {hard_stuck_epsilon}") + if hard_stuck_penalty < 0.0: + raise ValueError(f"hard_stuck_penalty must be >= 0.0, got {hard_stuck_penalty}") + + self.time_penalty = float(time_penalty) + self.hard_stuck_steps = int(hard_stuck_steps) + self.hard_stuck_epsilon = float(hard_stuck_epsilon) + self.hard_stuck_penalty = float(hard_stuck_penalty) + self._last_x_pos: Optional[float] = None + self._hard_stuck_count = 0 + + def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None): + del options + obs, info = reset_compat(self.env, seed=seed) + self._last_x_pos = float(info.get("x_pos", 0.0)) + self._hard_stuck_count = 0 + return obs, info + + def step(self, action: Any): + obs, reward, terminated, truncated, info = step_compat(self.env, action) + x_pos = float(info.get("x_pos", 0.0)) + if self._last_x_pos is None: + delta_x = 0.0 + else: + delta_x = x_pos - self._last_x_pos + self._last_x_pos = x_pos + + shaped_reward = float(reward) + if self.time_penalty != 0.0: + shaped_reward += self.time_penalty + + if self.hard_stuck_steps > 0 and not terminated and not truncated: + if abs(delta_x) < self.hard_stuck_epsilon: + self._hard_stuck_count += 1 + else: + self._hard_stuck_count = 0 + + if self._hard_stuck_count >= self.hard_stuck_steps: + shaped_reward -= self.hard_stuck_penalty + truncated = True + info["terminated_by_hard_stuck"] = True + info["done_reason"] = "hard_stuck" + else: + self._hard_stuck_count = 0 + + return obs, shaped_reward, terminated, truncated, info + + def get_action_set(name: str): name = name.lower().strip() if name == "simple": @@ -249,6 +315,10 @@ def make_mario_env( milestone_bonus: float = 1.0, no_progress_terminate_steps: int = 120, no_progress_terminate_penalty: float = 20.0, + time_penalty: float = 0.0, + hard_stuck_steps: int = 0, + hard_stuck_epsilon: float = 1.0, + hard_stuck_penalty: float = 5.0, ) -> gym.Env: kwargs: Dict[str, Any] = {} if render_mode is not None: @@ -284,6 +354,15 @@ def make_mario_env( elif mode != "raw": raise ValueError(f"Unsupported reward_mode='{reward_mode}'. Use one of: raw, clip, progress") + if time_penalty != 0.0 or hard_stuck_steps > 0: + env = TimePenaltyHardStuckEnv( + env=env, + time_penalty=time_penalty, + hard_stuck_steps=hard_stuck_steps, + hard_stuck_epsilon=hard_stuck_epsilon, + hard_stuck_penalty=hard_stuck_penalty, + ) + env = PreprocessFrame(env, width=84, height=84) env = ChannelLastFrameStack(env, num_stack=4) env = TransposeObservation(env) @@ -312,6 +391,10 @@ def make_env_fn( milestone_bonus: float, no_progress_terminate_steps: int, no_progress_terminate_penalty: float, + time_penalty: float, + hard_stuck_steps: int, + hard_stuck_epsilon: float, + hard_stuck_penalty: float, ) -> Callable[[], gym.Env]: def _thunk() -> gym.Env: return make_mario_env( @@ -332,6 +415,10 @@ def make_env_fn( milestone_bonus=milestone_bonus, no_progress_terminate_steps=no_progress_terminate_steps, no_progress_terminate_penalty=no_progress_terminate_penalty, + time_penalty=time_penalty, + hard_stuck_steps=hard_stuck_steps, + hard_stuck_epsilon=hard_stuck_epsilon, + hard_stuck_penalty=hard_stuck_penalty, ) return _thunk diff --git a/mario-rl-mvp/src/eval.py b/mario-rl-mvp/src/eval.py index b903506..d9bd076 100644 --- a/mario-rl-mvp/src/eval.py +++ b/mario-rl-mvp/src/eval.py @@ -6,6 +6,11 @@ from pathlib import Path from statistics import mean import numpy as np +from src.policy_utils import select_action +from src.runtime import configure_runtime_env + +configure_runtime_env() + from stable_baselines3 import PPO from src.env import get_action_set, make_mario_env, reset_compat, step_compat @@ -32,8 +37,51 @@ def parse_args() -> argparse.Namespace: parser.add_argument("--milestone-bonus", type=float, default=1.0) parser.add_argument("--no-progress-terminate-steps", type=int, default=120) parser.add_argument("--no-progress-terminate-penalty", type=float, default=20.0) + parser.add_argument( + "--time-penalty", + type=float, + default=0.0, + help="Per-step living cost added to reward (<=0.0). 0.0 disables.", + ) + parser.add_argument( + "--hard-stuck-steps", + type=int, + default=0, + help="Consecutive near-stationary steps before truncation. 0 disables.", + ) + parser.add_argument( + "--hard-stuck-epsilon", + type=float, + default=1.0, + help="Treat |x_pos delta| < epsilon as no movement for hard-stuck detection.", + ) + parser.add_argument( + "--hard-stuck-penalty", + type=float, + default=5.0, + help="Extra penalty applied when hard-stuck truncation triggers.", + ) parser.add_argument("--clip-reward", action="store_true") parser.add_argument("--stochastic", action="store_true", help="Use stochastic policy (deterministic=False).") + parser.add_argument( + "--epsilon", + type=float, + default=0.0, + help="Epsilon-greedy probability for deterministic policy. Ignored when --stochastic is set.", + ) + parser.add_argument( + "--epsilon-random-mode", + type=str, + default="uniform", + choices=["uniform", "policy"], + help="Random action source for epsilon-greedy: uniform=action_space.sample, policy=model stochastic sample.", + ) + parser.add_argument( + "--random-noops", + type=int, + default=0, + help="Random no-op steps after reset (0 disables). Uses action 0 as NOOP.", + ) return parser.parse_args() @@ -87,6 +135,8 @@ def resolve_model_path(user_path: str) -> Path: def main() -> None: args = parse_args() + if args.epsilon < 0.0 or args.epsilon > 1.0: + raise ValueError(f"--epsilon must be in [0, 1], got {args.epsilon}") seed_everything(args.seed) reward_mode = "clip" if args.clip_reward and args.reward_mode == "raw" else args.reward_mode @@ -94,7 +144,12 @@ def main() -> None: print(f"[eval] model={model_path}") model = PPO.load(str(model_path)) movement = resolve_movement(args.movement, model) - print(f"[eval] movement={movement} reward_mode={reward_mode}") + print( + f"[eval] movement={movement} reward_mode={reward_mode} random_noops={args.random_noops} " + f"time_penalty={args.time_penalty} hard_stuck_steps={args.hard_stuck_steps} " + f"hard_stuck_epsilon={args.hard_stuck_epsilon} hard_stuck_penalty={args.hard_stuck_penalty} " + f"epsilon={args.epsilon} epsilon_random_mode={args.epsilon_random_mode}" + ) env = make_mario_env( env_id=args.env_id, @@ -114,6 +169,10 @@ def main() -> None: milestone_bonus=args.milestone_bonus, no_progress_terminate_steps=args.no_progress_terminate_steps, no_progress_terminate_penalty=args.no_progress_terminate_penalty, + time_penalty=args.time_penalty, + hard_stuck_steps=args.hard_stuck_steps, + hard_stuck_epsilon=args.hard_stuck_epsilon, + hard_stuck_penalty=args.hard_stuck_penalty, ) rewards = [] @@ -122,6 +181,12 @@ def main() -> None: for ep in range(1, args.episodes + 1): obs, info = reset_compat(env, seed=args.seed + ep) + if args.random_noops > 0: + noop_steps = np.random.randint(0, args.random_noops + 1) + for _ in range(noop_steps): + obs, _, terminated, truncated, info = step_compat(env, 0) + if terminated or truncated: + obs, info = reset_compat(env, seed=args.seed + ep + 1000) done = False ep_reward = 0.0 ep_max_x = float(info.get("x_pos", 0.0)) @@ -129,9 +194,14 @@ def main() -> None: step_count = 0 while not done and step_count < args.max_steps: - action, _ = model.predict(obs, deterministic=not args.stochastic) - if isinstance(action, np.ndarray): - action = int(action.item()) + action = select_action( + model=model, + obs=obs, + deterministic=not args.stochastic, + epsilon=args.epsilon, + epsilon_random_mode=args.epsilon_random_mode, + env=env, + ) obs, reward, terminated, truncated, info = step_compat(env, action) ep_reward += float(reward) ep_max_x = max(ep_max_x, float(info.get("x_pos", 0.0))) diff --git a/mario-rl-mvp/src/plot_model_max_x_trend.py b/mario-rl-mvp/src/plot_model_max_x_trend.py new file mode 100644 index 0000000..f6f8a70 --- /dev/null +++ b/mario-rl-mvp/src/plot_model_max_x_trend.py @@ -0,0 +1,677 @@ +from __future__ import annotations + +import argparse +import datetime as dt +import html +import json +import re +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, List, Optional, Sequence, Tuple + +from tensorboard.backend.event_processing.event_accumulator import EventAccumulator + +from src.utils import ensure_artifact_paths + +RUN_TS_PATTERN = re.compile(r"(\d{8}_\d{6})$") +CKPT_STEPS_PATTERN = re.compile(r"ppo_mario_ckpt_(\d+)_steps\.zip$") + + +@dataclass(frozen=True) +class ScalarPoint: + step: int + value: float + + +@dataclass(frozen=True) +class MetricSpec: + key: str + label: str + tag_candidates: Tuple[str, ...] + meaning: str + scale: float = 1.0 + decimals: int = 2 + suffix: str = "" + + +METRIC_SPECS: List[MetricSpec] = [ + MetricSpec( + key="max_x", + label="最大前进距离", + tag_candidates=("rollout/episode_max_x_pos", "episode_end/episode_max_x_pos"), + meaning="马里奥在单回合内到达的最远 x 坐标，越大通常代表策略能跑得更远。", + decimals=1, + ), + MetricSpec( + key="ep_rew", + label="平均回报", + tag_candidates=("rollout/ep_rew_mean",), + meaning="训练窗口内的平均 episode reward，会随 reward shaping 配置变化。", + decimals=2, + ), + MetricSpec( + key="ep_len", + label="平均回合步数", + tag_candidates=("rollout/ep_len_mean",), + meaning="训练窗口内平均每回合步数；若步数高但 max_x 不涨，常见于卡住。", + decimals=1, + ), + MetricSpec( + key="clear_rate", + label="通关率", + tag_candidates=("rollout/flag_get", "episode_end/flag_get"), + meaning="到达旗帜终点的比例。0 表示从未通关，1 表示全部通关。", + scale=100.0, + decimals=1, + suffix="%", + ), + MetricSpec( + key="no_progress_rate", + label="无进展终止率", + tag_candidates=("rollout/done_reason_no_progress", "episode_end/done_reason_no_progress"), + meaning="因 no-progress 规则提前结束的比例，越高说明越容易卡住。", + scale=100.0, + decimals=1, + suffix="%", + ), + MetricSpec( + key="death_rate", + label="死亡终止率", + tag_candidates=("rollout/done_reason_death", "episode_end/done_reason_death"), + meaning="因死亡结束 episode 的比例。", + scale=100.0, + decimals=1, + suffix="%", + ), + MetricSpec( + key="timeout_rate", + label="超时终止率", + tag_candidates=("rollout/done_reason_timeout", "episode_end/done_reason_timeout"), + meaning="因时间耗尽或 TimeLimit 截断的比例。", + scale=100.0, + decimals=1, + suffix="%", + ), + MetricSpec( + key="hard_stuck_rate", + label="硬卡死终止率", + tag_candidates=("rollout/done_reason_hard_stuck", "episode_end/done_reason_hard_stuck"), + meaning="触发 hard-stuck 截断的比例（仅在启用 hard_stuck 后有数据）。", + scale=100.0, + decimals=1, + suffix="%", + ), +] +METRIC_BY_KEY: Dict[str, MetricSpec] = {spec.key: spec for spec in METRIC_SPECS} + + +@dataclass(frozen=True) +class ModelItem: + run_name: str + run_time: dt.datetime + model_path: Path + model_kind: str + model_step: Optional[int] + + +@dataclass(frozen=True) +class ModelMetric: + index: int + run_name: str + model_file: str + model_path: str + model_kind: str + model_step: Optional[int] + metric_values: Dict[str, Optional[float]] + metric_steps: Dict[str, Optional[int]] + metric_tags: Dict[str, Optional[str]] + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="可视化 artifacts/models 中模型在多维指标上的变化趋势，输出中文 HTML 或 Markdown 报告。" + ) + parser.add_argument("--models-dir", type=str, default="", help="模型目录，默认 artifacts/models") + parser.add_argument("--logs-dir", type=str, default="", help="日志目录，默认 artifacts/logs") + parser.add_argument( + "--format", + type=str, + default="html", + choices=("html", "markdown"), + help="输出格式：html 或 markdown", + ) + parser.add_argument( + "--output", + type=str, + default="", + help="输出报告路径。默认 html: artifacts/reports/model_max_x_trend.html，markdown: artifacts/reports/model_max_x_trend.md", + ) + return parser.parse_args() + + +def _parse_run_time(run_name: str, fallback: float) -> dt.datetime: + match = RUN_TS_PATTERN.search(run_name) + if match: + try: + return dt.datetime.strptime(match.group(1), "%Y%m%d_%H%M%S") + except ValueError: + pass + return dt.datetime.fromtimestamp(fallback) + + +def _list_model_items(models_dir: Path) -> List[ModelItem]: + items: List[ModelItem] = [] + for run_dir in sorted(path for path in models_dir.iterdir() if path.is_dir()): + run_name = run_dir.name + run_time = _parse_run_time(run_name, run_dir.stat().st_mtime) + + zip_files = sorted(path for path in run_dir.glob("*.zip") if path.is_file()) + staged: List[Tuple[int, str, Path, Optional[int], str]] = [] + for zip_path in zip_files: + name = zip_path.name + ckpt_match = CKPT_STEPS_PATTERN.match(name) + if ckpt_match: + step = int(ckpt_match.group(1)) + staged.append((0, f"{step:012d}", zip_path, step, "checkpoint")) + continue + if name == "final_model.zip": + staged.append((1, "final_model", zip_path, None, "final")) + continue + staged.append((2, name, zip_path, None, "other")) + + for _, _, model_path, model_step, model_kind in sorted(staged, key=lambda row: (row[0], row[1])): + items.append( + ModelItem( + run_name=run_name, + run_time=run_time, + model_path=model_path, + model_kind=model_kind, + model_step=model_step, + ) + ) + + items.sort( + key=lambda item: ( + item.run_time, + 10**18 if item.model_step is None and item.model_kind == "final" else (item.model_step or 0), + item.model_path.name, + ) + ) + return items + + +def _load_scalar_map_from_dir(event_dir: Path) -> Dict[str, List[ScalarPoint]]: + if not event_dir.exists(): + return {} + event_files = list(event_dir.glob("events.out.tfevents.*")) + if not event_files: + return {} + + accumulator = EventAccumulator(str(event_dir), size_guidance={"scalars": 0}) + try: + accumulator.Reload() + except Exception: + return {} + + scalar_map: Dict[str, List[ScalarPoint]] = {} + for tag in accumulator.Tags().get("scalars", []): + try: + points = [ScalarPoint(step=int(s.step), value=float(s.value)) for s in accumulator.Scalars(tag)] + except Exception: + continue + if points: + scalar_map[tag] = points + return scalar_map + + +def _load_run_scalar_map(logs_dir: Path, run_name: str) -> Dict[str, List[ScalarPoint]]: + run_dir = logs_dir / run_name + merged: Dict[str, List[ScalarPoint]] = {} + + for sub in (("tb", "ppo_1"), ("tb", "episode_end")): + event_dir = run_dir.joinpath(*sub) + part = _load_scalar_map_from_dir(event_dir) + for tag, points in part.items(): + if tag not in merged or len(points) > len(merged[tag]): + merged[tag] = points + + return merged + + +def _value_at_or_before(points: Sequence[ScalarPoint], target_step: int) -> Tuple[float, int]: + best = points[0] + for point in points: + if point.step <= target_step: + best = point + else: + break + return best.value, best.step + + +def _pick_series_for_metric( + scalar_map: Dict[str, List[ScalarPoint]], + spec: MetricSpec, +) -> Tuple[Optional[str], Optional[List[ScalarPoint]]]: + for tag in spec.tag_candidates: + points = scalar_map.get(tag) + if points: + return tag, points + return None, None + + +def _resolve_model_metric(item: ModelItem, scalar_map: Dict[str, List[ScalarPoint]], index: int) -> ModelMetric: + metric_values: Dict[str, Optional[float]] = {} + metric_steps: Dict[str, Optional[int]] = {} + metric_tags: Dict[str, Optional[str]] = {} + + for spec in METRIC_SPECS: + tag, points = _pick_series_for_metric(scalar_map, spec) + metric_tags[spec.key] = tag + if not points: + metric_values[spec.key] = None + metric_steps[spec.key] = None + continue + + if item.model_step is None: + point = points[-1] + metric_values[spec.key] = point.value + metric_steps[spec.key] = point.step + continue + + value, matched_step = _value_at_or_before(points, item.model_step) + metric_values[spec.key] = value + metric_steps[spec.key] = matched_step + + return ModelMetric( + index=index, + run_name=item.run_name, + model_file=item.model_path.name, + model_path=str(item.model_path.resolve()), + model_kind=item.model_kind, + model_step=item.model_step, + metric_values=metric_values, + metric_steps=metric_steps, + metric_tags=metric_tags, + ) + + +def _scaled_value(metric: ModelMetric, key: str) -> Optional[float]: + spec = METRIC_BY_KEY[key] + raw = metric.metric_values.get(key) + if raw is None: + return None + return raw * spec.scale + + +def _format_value(metric: ModelMetric, key: str) -> str: + spec = METRIC_BY_KEY[key] + scaled = _scaled_value(metric, key) + if scaled is None: + return "" + return f"{scaled:.{spec.decimals}f}{spec.suffix}" + + +def _render_metric_svg(metrics: Sequence[ModelMetric], key: str, width: int, height: int) -> str: + spec = METRIC_BY_KEY[key] + valid: List[Tuple[int, float]] = [] + for idx, metric in enumerate(metrics): + value = _scaled_value(metric, key) + if value is not None: + valid.append((idx, value)) + + if not valid: + return f'
{html.escape(spec.label)}：暂无可用数据
' + + margin_left = 58 + margin_right = 22 + margin_top = 18 + margin_bottom = 48 + plot_w = width - margin_left - margin_right + plot_h = height - margin_top - margin_bottom + + y_values = [v for _, v in valid] + y_min = min(0.0, min(y_values)) + y_max = max(y_values) + if y_max <= y_min: + y_max = y_min + 1.0 + + def sx(index: int) -> float: + if len(metrics) == 1: + return margin_left + plot_w / 2.0 + return margin_left + (index / (len(metrics) - 1)) * plot_w + + def sy(value: float) -> float: + ratio = (value - y_min) / (y_max - y_min) + return margin_top + (1.0 - ratio) * plot_h + + y_grid_count = 4 + y_grid: List[str] = [] + for i in range(y_grid_count + 1): + ratio = i / y_grid_count + y = margin_top + ratio * plot_h + value = y_max - ratio * (y_max - y_min) + y_grid.append( + f'' + ) + y_grid.append( + f'' + f"{value:.1f}{html.escape(spec.suffix)}" + ) + + points_attr = " ".join(f"{sx(idx):.2f},{sy(value):.2f}" for idx, value in valid) + polyline = ( + f'' + ) + + point_nodes: List[str] = [] + for idx, value in valid: + metric = metrics[idx] + point_nodes.append( + f'' + f"{html.escape(spec.label)}={value:.2f}{spec.suffix} | 序号={metric.index} | {metric.model_file}" + "" + ) + + return "\n".join( + [ + f'
{html.escape(spec.label)}趋势
', + f'', + "", + *y_grid, + f'', + f'', + polyline, + *point_nodes, + f'模型序号（按时间排序）', + "", + ] + ) + + +def _metric_payload(metric: ModelMetric) -> Dict[str, object]: + payload: Dict[str, object] = { + "index": metric.index, + "run_name": metric.run_name, + "model_file": metric.model_file, + "model_path": metric.model_path, + "model_kind": metric.model_kind, + "model_step": metric.model_step, + } + for spec in METRIC_SPECS: + payload[spec.key] = metric.metric_values.get(spec.key) + payload[f"{spec.key}_matched_step"] = metric.metric_steps.get(spec.key) + payload[f"{spec.key}_source_tag"] = metric.metric_tags.get(spec.key) + return payload + + +def _render_table(metrics: Sequence[ModelMetric]) -> str: + header = ( + "" + "序号训练Run模型文件类型checkpoint步数" + "最大前进距离平均回报平均回合步数通关率" + "无进展率死亡率超时率硬卡死率" + "max_x匹配步max_x来源Tag模型路径" + "" + ) + + rows: List[str] = [] + for metric in metrics: + rows.append( + "" + f"{metric.index}" + f"{html.escape(metric.run_name)}" + f"{html.escape(metric.model_file)}" + f"{html.escape(metric.model_kind)}" + f"{'' if metric.model_step is None else metric.model_step}" + f"{html.escape(_format_value(metric, 'max_x'))}" + f"{html.escape(_format_value(metric, 'ep_rew'))}" + f"{html.escape(_format_value(metric, 'ep_len'))}" + f"{html.escape(_format_value(metric, 'clear_rate'))}" + f"{html.escape(_format_value(metric, 'no_progress_rate'))}" + f"{html.escape(_format_value(metric, 'death_rate'))}" + f"{html.escape(_format_value(metric, 'timeout_rate'))}" + f"{html.escape(_format_value(metric, 'hard_stuck_rate'))}" + f"{'' if metric.metric_steps.get('max_x') is None else metric.metric_steps.get('max_x')}" + f"{html.escape(metric.metric_tags.get('max_x') or '')}" + f"{html.escape(metric.model_path)}" + "" + ) + + return f"{header}{''.join(rows)}
" + + +def _glossary_items() -> List[Tuple[str, str]]: + return [ + ("Run", "一次完整训练实验，目录名通常形如 ppo_..._YYYYMMDD_HHMMSS。"), + ("Checkpoint", "训练中间保存的模型快照，例如 ppo_mario_ckpt_100000_steps.zip。"), + ("Final Model", "训练结束时保存的最终模型（final_model.zip）。"), + ("model_step", "从 checkpoint 文件名解析出的训练步数。final_model 没有固定 step。"), + ("matched_step", "从 TensorBoard 曲线中用于取值的实际步数（不超过 model_step 的最近点）。"), + ("TensorBoard Tag", "标量指标的键名，例如 rollout/episode_max_x_pos。"), + ("max_x", "最远横坐标，表示策略在关卡中的推进深度。"), + ("ep_rew_mean", "平均回报，受 reward shaping 配置影响。"), + ("ep_len_mean", "平均回合步数。"), + ("flag_get / clear_rate", "通关比例（0~1，页面按百分比显示）。"), + ("done_reason_*", "不同终止原因的比例（例如 no_progress、death、timeout、hard_stuck）。"), + ] + + +def _render_glossary() -> str: + rows = "".join(f"
  • {html.escape(name)}：{html.escape(desc)}
    • " for name, desc in _glossary_items()) + return f"" + + +def _render_glossary_markdown() -> str: + return "\n".join(f"- **{name}**：{desc}" for name, desc in _glossary_items()) + + +def _summarize_metrics( + metrics: Sequence[ModelMetric], +) -> Tuple[int, int, Optional[float], Optional[ModelMetric], Optional[ModelMetric]]: + total = len(metrics) + with_max_x = sum(1 for metric in metrics if metric.metric_values.get("max_x") is not None) + + best_max_x = None + best_entry = None + for metric in metrics: + value = metric.metric_values.get("max_x") + if value is None: + continue + if best_max_x is None or value > best_max_x: + best_max_x = value + best_entry = metric + + latest_with_max = next((metric for metric in reversed(metrics) if metric.metric_values.get("max_x") is not None), None) + return total, with_max_x, best_max_x, best_entry, latest_with_max + + +def _build_best_text(best_max_x: Optional[float], best_entry: Optional[ModelMetric]) -> str: + if best_entry is None or best_max_x is None: + return "无" + return f"{best_max_x:.1f}（序号#{best_entry.index}，{best_entry.model_file}）" + + +def _build_latest_text(latest_with_max: Optional[ModelMetric]) -> str: + if latest_with_max is None: + return "无" + return f"{_format_value(latest_with_max, 'max_x')}（序号#{latest_with_max.index}，{latest_with_max.model_file}）" + + +def _md_escape(text: str) -> str: + return text.replace("|", "\\|").replace("\n", " ") + + +def build_html(metrics: Sequence[ModelMetric], models_dir: Path, logs_dir: Path) -> str: + total, with_max_x, best_max_x, best_entry, latest_with_max = _summarize_metrics(metrics) + + best_text = _build_best_text(best_max_x=best_max_x, best_entry=best_entry) + latest_text = _build_latest_text(latest_with_max=latest_with_max) + + main_chart = _render_metric_svg(metrics, key="max_x", width=1260, height=390) + extra_keys = ["ep_rew", "ep_len", "clear_rate", "no_progress_rate", "death_rate", "timeout_rate"] + extra_charts = "".join( + f'
    {_render_metric_svg(metrics, key=key, width=610, height=250)}
    ' for key in extra_keys + ) + + table = _render_table(metrics) + glossary = _render_glossary() + payload = json.dumps([_metric_payload(metric) for metric in metrics], ensure_ascii=False, indent=2) + + return f""" + + + + + 模型趋势报告（max_x + 多维指标） + + + +

    模型趋势报告（max_x + 多维指标）

    +
    +
    模型目录：{html.escape(str(models_dir.resolve()))}
    +
    日志目录：{html.escape(str(logs_dir.resolve()))}
    +
    模型总数：{total}；有 max_x 数据：{with_max_x}
    +
    历史最佳 max_x：{html.escape(best_text)}
    +
    最近模型 max_x：{html.escape(latest_text)}
    +
    说明：模型序号按时间排序（run 时间 + checkpoint 步数）。鼠标悬停图中点可看细节。
    +
    + +
    +

    主趋势：最大前进距离（max_x）

    + {main_chart} +
    + +
    {extra_charts}
    + +
    +

    模型明细表

    + {table} +
    + +
    +

    术语解释（专有名词）

    + {glossary} +
    + +
    + 原始数据（JSON） + 
    {html.escape(payload)}
    +
    + + +""" + + +def build_markdown(metrics: Sequence[ModelMetric], models_dir: Path, logs_dir: Path) -> str: + total, with_max_x, best_max_x, best_entry, latest_with_max = _summarize_metrics(metrics) + best_text = _build_best_text(best_max_x=best_max_x, best_entry=best_entry) + latest_text = _build_latest_text(latest_with_max=latest_with_max) + glossary = _render_glossary_markdown() + payload = json.dumps([_metric_payload(metric) for metric in metrics], ensure_ascii=False, indent=2) + + lines: List[str] = [] + lines.append("# 模型趋势报告（max_x + 多维指标）") + lines.append("") + lines.append("## 概览") + lines.append(f"- 模型目录：`{models_dir.resolve()}`") + lines.append(f"- 日志目录：`{logs_dir.resolve()}`") + lines.append(f"- 模型总数：**{total}**") + lines.append(f"- 含 max_x 数据：**{with_max_x}**") + lines.append(f"- 历史最佳 max_x：**{best_text}**") + lines.append(f"- 最近模型 max_x：**{latest_text}**") + lines.append("- 说明：模型序号按时间排序（run 时间 + checkpoint 步数）。") + lines.append("") + lines.append("## 指标说明") + for spec in METRIC_SPECS: + lines.append(f"- **{spec.label}**（`{spec.key}`）：{spec.meaning}") + lines.append("") + lines.append("## 模型明细") + lines.append( + "| 序号 | Run | 模型文件 | 类型 | checkpoint步数 | 最大前进距离 | 平均回报 | 平均回合步数 | 通关率 | 无进展率 | 死亡率 | 超时率 | 硬卡死率 | max_x匹配步 | max_x来源Tag |" + ) + lines.append("|---:|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---|") + for metric in metrics: + lines.append( + "| " + f"{metric.index} | " + f"{_md_escape(metric.run_name)} | " + f"{_md_escape(metric.model_file)} | " + f"{metric.model_kind} | " + f"{'' if metric.model_step is None else metric.model_step} | " + f"{_format_value(metric, 'max_x')} | " + f"{_format_value(metric, 'ep_rew')} | " + f"{_format_value(metric, 'ep_len')} | " + f"{_format_value(metric, 'clear_rate')} | " + f"{_format_value(metric, 'no_progress_rate')} | " + f"{_format_value(metric, 'death_rate')} | " + f"{_format_value(metric, 'timeout_rate')} | " + f"{_format_value(metric, 'hard_stuck_rate')} | " + f"{'' if metric.metric_steps.get('max_x') is None else metric.metric_steps.get('max_x')} | " + f"{_md_escape(metric.metric_tags.get('max_x') or '')} |" + ) + lines.append("") + lines.append("## 术语解释（专有名词）") + lines.append(glossary) + lines.append("") + lines.append("## 原始数据（JSON）") + lines.append("```json") + lines.append(payload) + lines.append("```") + return "\n".join(lines) + + +def main() -> None: + args = parse_args() + paths = ensure_artifact_paths() + + models_dir = Path(args.models_dir).expanduser().resolve() if args.models_dir else paths.models.resolve() + logs_dir = Path(args.logs_dir).expanduser().resolve() if args.logs_dir else paths.logs.resolve() + default_name = "model_max_x_trend.md" if args.format == "markdown" else "model_max_x_trend.html" + output_path = ( + Path(args.output).expanduser().resolve() + if args.output + else (paths.root / "reports" / default_name).resolve() + ) + output_path.parent.mkdir(parents=True, exist_ok=True) + + model_items = _list_model_items(models_dir) + scalar_cache: Dict[str, Dict[str, List[ScalarPoint]]] = {} + metrics: List[ModelMetric] = [] + + for idx, item in enumerate(model_items, start=1): + if item.run_name not in scalar_cache: + scalar_cache[item.run_name] = _load_run_scalar_map(logs_dir=logs_dir, run_name=item.run_name) + metrics.append(_resolve_model_metric(item=item, scalar_map=scalar_cache[item.run_name], index=idx)) + + if args.format == "markdown": + content = build_markdown(metrics=metrics, models_dir=models_dir, logs_dir=logs_dir) + else: + content = build_html(metrics=metrics, models_dir=models_dir, logs_dir=logs_dir) + output_path.write_text(content, encoding="utf-8") + + with_max_x = sum(1 for metric in metrics if metric.metric_values.get("max_x") is not None) + print(f"[趋势] 报告已生成：{output_path} (format={args.format})") + print(f"[趋势] 模型总数={len(metrics)}，含max_x数据={with_max_x}") + + +if __name__ == "__main__": + main() diff --git a/mario-rl-mvp/src/policy_utils.py b/mario-rl-mvp/src/policy_utils.py new file mode 100644 index 0000000..32a5fd3 --- /dev/null +++ b/mario-rl-mvp/src/policy_utils.py @@ -0,0 +1,53 @@ +from __future__ import annotations + +from typing import Any, Optional + +import numpy as np + + +def _as_discrete_action(action: Any) -> int: + if isinstance(action, np.ndarray): + return int(action.item()) + if isinstance(action, np.generic): + return int(action.item()) + try: + return int(action) + except (TypeError, ValueError) as exc: + raise ValueError(f"Cannot convert action to int: {action!r}") from exc + + +def select_action( + model: Any, + obs: Any, + deterministic: bool, + epsilon: float = 0.0, + epsilon_random_mode: str = "uniform", + env: Optional[Any] = None, + rng: Optional[np.random.Generator] = None, +) -> int: + """Select action with optional epsilon-greedy for deterministic inference. + + When deterministic=False (stochastic policy), epsilon is ignored by design. + """ + + if epsilon < 0.0 or epsilon > 1.0: + raise ValueError(f"epsilon must be in [0, 1], got {epsilon}") + + mode = epsilon_random_mode.lower().strip() + if mode not in {"uniform", "policy"}: + raise ValueError(f"Unsupported epsilon_random_mode={epsilon_random_mode!r}. Use 'uniform' or 'policy'.") + + effective_epsilon = epsilon if deterministic else 0.0 + if effective_epsilon > 0.0: + draw = float(rng.random()) if rng is not None else float(np.random.random()) + if draw < effective_epsilon: + if mode == "uniform": + if env is None or not hasattr(env, "action_space"): + raise ValueError("uniform epsilon_random_mode requires env.action_space.sample().") + return _as_discrete_action(env.action_space.sample()) + action, _ = model.predict(obs, deterministic=False) + return _as_discrete_action(action) + + action, _ = model.predict(obs, deterministic=deterministic) + return _as_discrete_action(action) + diff --git a/mario-rl-mvp/src/record_video.py b/mario-rl-mvp/src/record_video.py index 49638e1..40c8f18 100644 --- a/mario-rl-mvp/src/record_video.py +++ b/mario-rl-mvp/src/record_video.py @@ -5,7 +5,11 @@ from datetime import datetime from pathlib import Path import imageio.v2 as imageio -import numpy as np +from src.policy_utils import select_action +from src.runtime import configure_runtime_env + +configure_runtime_env() + from stable_baselines3 import PPO from src.env import get_action_set, make_mario_env, reset_compat, step_compat @@ -30,11 +34,48 @@ def parse_args() -> argparse.Namespace: parser.add_argument("--milestone-bonus", type=float, default=1.0) parser.add_argument("--no-progress-terminate-steps", type=int, default=120) parser.add_argument("--no-progress-terminate-penalty", type=float, default=20.0) + parser.add_argument( + "--time-penalty", + type=float, + default=0.0, + help="Per-step living cost added to reward (<=0.0). 0.0 disables.", + ) + parser.add_argument( + "--hard-stuck-steps", + type=int, + default=0, + help="Consecutive near-stationary steps before truncation. 0 disables.", + ) + parser.add_argument( + "--hard-stuck-epsilon", + type=float, + default=1.0, + help="Treat |x_pos delta| < epsilon as no movement for hard-stuck detection.", + ) + parser.add_argument( + "--hard-stuck-penalty", + type=float, + default=5.0, + help="Extra penalty applied when hard-stuck truncation triggers.", + ) parser.add_argument("--clip-reward", action="store_true") parser.add_argument("--fps", type=int, default=30) parser.add_argument("--duration-sec", type=int, default=10) parser.add_argument("--max-steps", type=int, default=5_000) parser.add_argument("--stochastic", action="store_true") + parser.add_argument( + "--epsilon", + type=float, + default=0.0, + help="Epsilon-greedy probability for deterministic policy. Ignored when --stochastic is set.", + ) + parser.add_argument( + "--epsilon-random-mode", + type=str, + default="uniform", + choices=["uniform", "policy"], + help="Random action source for epsilon-greedy: uniform=action_space.sample, policy=model stochastic sample.", + ) parser.add_argument("--output", type=str, default="", help="Output mp4 path.") return parser.parse_args() @@ -106,6 +147,8 @@ def save_frames_fallback(frames, output_path: Path) -> Path: def main() -> None: args = parse_args() + if args.epsilon < 0.0 or args.epsilon > 1.0: + raise ValueError(f"--epsilon must be in [0, 1], got {args.epsilon}") seed_everything(args.seed) reward_mode = "clip" if args.clip_reward and args.reward_mode == "raw" else args.reward_mode @@ -116,7 +159,12 @@ def main() -> None: model = PPO.load(str(model_path)) print(f"[video] model={model_path}") movement = resolve_movement(args.movement, model) - print(f"[video] movement={movement} reward_mode={reward_mode}") + print( + f"[video] movement={movement} reward_mode={reward_mode} " + f"time_penalty={args.time_penalty} hard_stuck_steps={args.hard_stuck_steps} " + f"hard_stuck_epsilon={args.hard_stuck_epsilon} hard_stuck_penalty={args.hard_stuck_penalty} " + f"epsilon={args.epsilon} epsilon_random_mode={args.epsilon_random_mode}" + ) env = make_mario_env( env_id=args.env_id, seed=args.seed, @@ -135,6 +183,10 @@ def main() -> None: milestone_bonus=args.milestone_bonus, no_progress_terminate_steps=args.no_progress_terminate_steps, no_progress_terminate_penalty=args.no_progress_terminate_penalty, + time_penalty=args.time_penalty, + hard_stuck_steps=args.hard_stuck_steps, + hard_stuck_epsilon=args.hard_stuck_epsilon, + hard_stuck_penalty=args.hard_stuck_penalty, ) obs, _ = reset_compat(env, seed=args.seed) @@ -152,9 +204,14 @@ def main() -> None: episode_count = 1 while len(frames) < target_frames and step_count < args.max_steps: - action, _ = model.predict(obs, deterministic=not args.stochastic) - if isinstance(action, np.ndarray): - action = int(action.item()) + action = select_action( + model=model, + obs=obs, + deterministic=not args.stochastic, + epsilon=args.epsilon, + epsilon_random_mode=args.epsilon_random_mode, + env=env, + ) obs, reward, terminated, truncated, info = step_compat(env, action) reward_sum += float(reward) step_count += 1 diff --git a/mario-rl-mvp/src/runtime.py b/mario-rl-mvp/src/runtime.py new file mode 100644 index 0000000..a22a742 --- /dev/null +++ b/mario-rl-mvp/src/runtime.py @@ -0,0 +1,14 @@ +from __future__ import annotations + +import os +from pathlib import Path + + +def configure_runtime_env() -> None: + """Set runtime defaults that are safe on restricted Linux/WSL setups.""" + if "MPLCONFIGDIR" in os.environ: + return + + cache_dir = Path.cwd() / ".cache" / "matplotlib" + cache_dir.mkdir(parents=True, exist_ok=True) + os.environ["MPLCONFIGDIR"] = str(cache_dir) diff --git a/mario-rl-mvp/src/train_ppo.py b/mario-rl-mvp/src/train_ppo.py index 5ea4b18..b82a604 100644 --- a/mario-rl-mvp/src/train_ppo.py +++ b/mario-rl-mvp/src/train_ppo.py @@ -5,6 +5,10 @@ import shutil from datetime import datetime from pathlib import Path +from src.runtime import configure_runtime_env + +configure_runtime_env() + from stable_baselines3 import PPO from stable_baselines3.common.callbacks import BaseCallback, CallbackList, CheckpointCallback from stable_baselines3.common.save_util import load_from_zip_file @@ -12,7 +16,13 @@ from stable_baselines3.common.vec_env import DummyVecEnv, VecMonitor from torch.utils.tensorboard import SummaryWriter from src.env import make_env_fn -from src.utils import ensure_artifact_paths, resolve_torch_device, seed_everything, write_latest_pointer +from src.utils import ( + collect_torch_runtime_diagnostics, + ensure_artifact_paths, + resolve_torch_device, + seed_everything, + write_latest_pointer, +) def parse_args() -> argparse.Namespace: @@ -34,6 +44,30 @@ def parse_args() -> argparse.Namespace: parser.add_argument("--milestone-bonus", type=float, default=1.0) parser.add_argument("--no-progress-terminate-steps", type=int, default=120) parser.add_argument("--no-progress-terminate-penalty", type=float, default=20.0) + parser.add_argument( + "--time-penalty", + type=float, + default=0.0, + help="Per-step living cost added to reward (<=0.0). 0.0 disables.", + ) + parser.add_argument( + "--hard-stuck-steps", + type=int, + default=0, + help="Consecutive near-stationary steps before truncation. 0 disables.", + ) + parser.add_argument( + "--hard-stuck-epsilon", + type=float, + default=1.0, + help="Treat |x_pos delta| < epsilon as no movement for hard-stuck detection.", + ) + parser.add_argument( + "--hard-stuck-penalty", + type=float, + default=5.0, + help="Extra penalty applied when hard-stuck truncation triggers.", + ) parser.add_argument("--learning-rate", type=float, default=2.5e-4) parser.add_argument("--n-steps", type=int, default=128) @@ -51,6 +85,11 @@ def parse_args() -> argparse.Namespace: default="auto", help="Torch device: auto | cpu | mps | cuda | cuda:0 ...", ) + parser.add_argument( + "--allow-device-fallback", + action="store_true", + help="Allow explicit --device requests (cuda/mps) to fallback to CPU when unavailable.", + ) parser.add_argument("--clip-reward", action="store_true", help="Enable reward clipping to {-1,0,1}.") parser.add_argument("--run-name", type=str, default="", help="Optional custom run name.") parser.add_argument( @@ -98,7 +137,7 @@ def load_partial_policy_weights(model: PPO, init_model_path: Path, device: str) class EpisodeEndLoggingCallback(BaseCallback): """Log per-episode terminal diagnostics to stdout and TensorBoard.""" - REASON_TO_CODE = {"death": 0, "no_progress": 1, "timeout": 2, "clear": 3} + REASON_TO_CODE = {"death": 0, "no_progress": 1, "timeout": 2, "clear": 3, "hard_stuck": 4} def __init__(self, n_envs: int, tb_log_dir: Path): super().__init__(verbose=0) @@ -110,8 +149,13 @@ class EpisodeEndLoggingCallback(BaseCallback): @staticmethod def _resolve_done_reason(info: dict) -> str: + done_reason = str(info.get("done_reason", "")).strip().lower() + if done_reason in EpisodeEndLoggingCallback.REASON_TO_CODE: + return done_reason if bool(info.get("flag_get", False)): return "clear" + if bool(info.get("terminated_by_hard_stuck", False)): + return "hard_stuck" if bool(info.get("terminated_by_stall", False)): return "no_progress" if bool(info.get("TimeLimit.truncated", False)): @@ -164,6 +208,9 @@ class EpisodeEndLoggingCallback(BaseCallback): ) self.tb_writer.add_scalar("episode_end/done_reason_timeout", 1.0 if done_reason == "timeout" else 0.0, episode_step) self.tb_writer.add_scalar("episode_end/done_reason_clear", 1.0 if done_reason == "clear" else 0.0, episode_step) + self.tb_writer.add_scalar( + "episode_end/done_reason_hard_stuck", 1.0 if done_reason == "hard_stuck" else 0.0, episode_step + ) self.tb_writer.flush() print( @@ -196,12 +243,25 @@ def main() -> None: run_model_dir.mkdir(parents=True, exist_ok=True) run_tb_dir.mkdir(parents=True, exist_ok=True) + requested_device = args.device.lower().strip() device, device_msg = resolve_torch_device(args.device) print(f"[device] {device} | {device_msg}") + print(f"[device_diag] {collect_torch_runtime_diagnostics()}") + + explicit_device_requested = requested_device not in {"auto", "cpu"} + if explicit_device_requested and device == "cpu" and not args.allow_device_fallback: + raise RuntimeError( + f"Requested --device {args.device!r}, but it resolved to CPU. {device_msg}\n" + "Either fix your CUDA/MPS environment, or re-run with --allow-device-fallback " + "to continue on CPU intentionally." + ) + print(f"[run] {run_name}") print( f"[config] env_id={args.env_id}, movement={args.movement}, reward_mode={reward_mode}, " - f"clip_reward={args.clip_reward}, n_envs={args.n_envs}" + f"clip_reward={args.clip_reward}, n_envs={args.n_envs}, " + f"time_penalty={args.time_penalty}, hard_stuck_steps={args.hard_stuck_steps}, " + f"hard_stuck_epsilon={args.hard_stuck_epsilon}, hard_stuck_penalty={args.hard_stuck_penalty}" ) env_fns = [ @@ -222,6 +282,10 @@ def main() -> None: milestone_bonus=args.milestone_bonus, no_progress_terminate_steps=args.no_progress_terminate_steps, no_progress_terminate_penalty=args.no_progress_terminate_penalty, + time_penalty=args.time_penalty, + hard_stuck_steps=args.hard_stuck_steps, + hard_stuck_epsilon=args.hard_stuck_epsilon, + hard_stuck_penalty=args.hard_stuck_penalty, ) for i in range(args.n_envs) ] diff --git a/mario-rl-mvp/src/utils.py b/mario-rl-mvp/src/utils.py index d4ce232..345d160 100644 --- a/mario-rl-mvp/src/utils.py +++ b/mario-rl-mvp/src/utils.py @@ -1,6 +1,7 @@ from __future__ import annotations import random +import warnings from dataclasses import dataclass from pathlib import Path from typing import Optional, Tuple @@ -9,6 +10,16 @@ import numpy as np import torch +def cuda_is_available_quiet() -> bool: + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + message="CUDA initialization: Unexpected error from cudaGetDeviceCount\\(\\).*", + category=UserWarning, + ) + return torch.cuda.is_available() + + @dataclass(frozen=True) class ArtifactPaths: root: Path @@ -36,7 +47,7 @@ def seed_everything(seed: int) -> None: random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) - if torch.cuda.is_available(): + if cuda_is_available_quiet(): torch.cuda.manual_seed_all(seed) @@ -46,7 +57,7 @@ def resolve_torch_device(requested: str = "auto") -> Tuple[str, str]: return "cpu", "User requested CPU." def _probe_cuda(device_name: str = "cuda") -> Tuple[str, str]: - if not torch.cuda.is_available(): + if not cuda_is_available_quiet(): return "cpu", "CUDA unavailable, using CPU." try: x = torch.ones(8, device=device_name) @@ -90,6 +101,47 @@ def resolve_torch_device(requested: str = "auto") -> Tuple[str, str]: return "cpu", f"Unknown device '{requested}', fallback to CPU." +def collect_torch_runtime_diagnostics() -> str: + lines: list[str] = [] + lines.append(f"torch={torch.__version__}") + lines.append(f"torch.version.cuda={torch.version.cuda}") + + try: + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + message="CUDA initialization: Unexpected error from cudaGetDeviceCount\\(\\).*", + category=UserWarning, + ) + cuda_available = torch.cuda.is_available() + lines.append(f"torch.cuda.is_available()={cuda_available}") + except Exception as exc: # pragma: no cover - hardware dependent + cuda_available = False + lines.append(f"torch.cuda.is_available() raised={exc}") + + try: + cuda_count = torch.cuda.device_count() + lines.append(f"torch.cuda.device_count()={cuda_count}") + except Exception as exc: # pragma: no cover - hardware dependent + cuda_count = 0 + lines.append(f"torch.cuda.device_count() raised={exc}") + + if cuda_count > 0: + try: + lines.append(f"torch.cuda.get_device_name(0)={torch.cuda.get_device_name(0)}") + except Exception as exc: # pragma: no cover - hardware dependent + lines.append(f"torch.cuda.get_device_name(0) raised={exc}") + try: + lines.append(f"torch.cuda.get_device_capability(0)={torch.cuda.get_device_capability(0)}") + except Exception as exc: # pragma: no cover - hardware dependent + lines.append(f"torch.cuda.get_device_capability(0) raised={exc}") + + mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available() + lines.append(f"torch.backends.mps.is_available()={mps_available}") + + return " | ".join(lines) + + def latest_model_path(models_dir: Path) -> Optional[Path]: candidates = [p for p in models_dir.rglob("*.zip") if p.is_file()] if not candidates: