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Horldsence
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"""低层 PPO 训练：8 方向到达任务（BC 直线专家热启动 + PPO 微调）。
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产物：
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output/hrl/hrl_low.zip / hrl_low.json PPO 模型（部署用）
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output/hrl/bc_low_expert.npz / bc_low.pt 行为克隆数据与网络
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output/hrl/hrl_low_curve_env*.monitor.csv 每个环境独立学习曲线
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评估口径：到达成功率 + 路径效率（直线距离/实际路径长，1 为完美直线）。
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这是独立导航实验：目前低层 PPO 仅供轨迹诊断，不影响正式 HTTP 任务。
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示例：
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uv run python scripts/train_hrl_low.py --timesteps 60000
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uv run python scripts/train_hrl_low.py --skip-bc # 纯 PPO 对照
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"""
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from __future__ import annotations
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import argparse
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import hashlib
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import json
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import math
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import time
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from pathlib import Path
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from mcm26b.config import ProblemConfig
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from mcm26b.hrl.constants import (FEATURE_VERSION, LOW_MAX_STEPS,
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LOW_NUM_ACTIONS, LOW_OBS_DIM)
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from mcm26b.hrl.low_level_env import LowLevelEnv
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HIDDEN = (64, 64)
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def _bc_module(obs_dim: int, n_actions: int, hidden: tuple[int, ...]):
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import torch
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class BCMlp(torch.nn.Module):
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def __init__(self):
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super().__init__()
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layers = []
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prev = obs_dim
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for width in hidden:
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layers += [torch.nn.Linear(prev, width), torch.nn.Tanh()]
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prev = width
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layers.append(torch.nn.Linear(prev, n_actions))
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self.net = torch.nn.Sequential(*layers)
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def forward(self, x):
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return self.net(x)
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return BCMlp
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def collect(episodes: int, seed: int, config: ProblemConfig):
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import numpy as np
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env = LowLevelEnv(config=config, seed=seed)
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observations, actions, episode_ids = [], [], []
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for episode in range(episodes):
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obs, _info = env.reset(seed=seed + episode)
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for _step in range(LOW_MAX_STEPS + 2):
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observations.append(obs)
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actions.append(env.expert_action())
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episode_ids.append(episode)
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obs, _r, terminated, truncated, _info = env.step(env.expert_action())
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if terminated or truncated:
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break
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env.close()
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return (np.asarray(observations, dtype=np.float32),
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np.asarray(actions, dtype=np.int64),
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np.asarray(episode_ids, dtype=np.int64))
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def bc_validation_mask(episode_ids, *, seed: int):
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"""按整局留出，避免同一轨迹相邻状态跨训练/验证集。"""
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import numpy as np
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unique = np.unique(episode_ids)
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if len(unique) < 2:
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raise ValueError("BC 至少需要 2 局才能按整局划分训练/验证集")
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rng = np.random.default_rng(seed)
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validation_ids = rng.permutation(unique)[:max(1, len(unique) // 10)]
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return np.isin(episode_ids, validation_ids)
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def train_bc(x, y, episode_ids, *, epochs: int, batch_size: int, lr: float,
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device: str, seed: int):
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import numpy as np
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import torch
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BCMlp = _bc_module(LOW_OBS_DIM, LOW_NUM_ACTIONS, HIDDEN)
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model = BCMlp().to(device)
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optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
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valid = torch.as_tensor(
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bc_validation_mask(episode_ids, seed=seed), device=device)
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x_t = torch.as_tensor(x, device=device)
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y_t = torch.as_tensor(y, device=device)
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x_v, y_v, x_tr, y_tr = x_t[valid], y_t[valid], x_t[~valid], y_t[~valid]
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loss_fn = torch.nn.CrossEntropyLoss()
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for epoch in range(epochs):
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order = torch.randperm(len(x_tr), device=device)
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total = correct = 0.0
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for start in range(0, len(order), batch_size):
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idx = order[start:start + batch_size]
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logits = model(x_tr[idx])
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loss = loss_fn(logits, y_tr[idx])
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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total += float(loss.item()) * len(idx)
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correct += int((logits.argmax(1) == y_tr[idx]).sum().item())
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if epoch == 0 or (epoch + 1) % 5 == 0:
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with torch.no_grad():
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valid_acc = float((model(x_v).argmax(1) == y_v).float().mean().item())
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print(json.dumps({"event": "bc_epoch", "epoch": epoch + 1,
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"loss": round(total / len(x_tr), 4),
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"train_acc": round(correct / len(x_tr), 4),
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"valid_acc": round(valid_acc, 4)},
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ensure_ascii=False))
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with torch.no_grad():
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valid_acc = float((model(x_v).argmax(1) == y_v).float().mean().item())
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return model, valid_acc
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def copy_into_ppo(bc_model, device: str, env, *, seed: int,
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n_steps: int = 512) -> object:
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"""把 BC 权重拷入 MaskablePPO（policy_net 两层 + action_net），前向校验。"""
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import torch
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from sb3_contrib import MaskablePPO
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ppo = MaskablePPO("MlpPolicy", env, policy_kwargs={"net_arch": list(HIDDEN)},
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n_steps=n_steps, batch_size=128, n_epochs=6,
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gamma=1.0, gae_lambda=0.95, learning_rate=3e-4,
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clip_range=0.2, ent_coef=0.005, vf_coef=0.5,
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max_grad_norm=0.5, seed=seed, device=device)
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state = {key.removeprefix("net."): value
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for key, value in bc_model.state_dict().items()}
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policy_state = {key: value for key, value in state.items()
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if key in ("0.weight", "0.bias", "2.weight", "2.bias")}
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ppo.policy.mlp_extractor.policy_net.load_state_dict(policy_state)
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ppo.policy.action_net.load_state_dict(
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{"weight": state["4.weight"], "bias": state["4.bias"]})
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probe = torch.randn((8, LOW_OBS_DIM), device=device)
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with torch.no_grad():
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copied = ppo.policy.action_net(ppo.policy.mlp_extractor.policy_net(probe))
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if not torch.allclose(bc_model(probe), copied, atol=1e-6):
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raise RuntimeError("行为克隆与 PPO 前向映射不一致")
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return ppo
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def evaluate(model, *, episodes: int, seed: int,
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config: ProblemConfig) -> dict:
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"""确定性策略评估：成功率与路径效率（对照直线）。"""
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env = LowLevelEnv(config=config, seed=seed)
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successes = 0
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efficiencies = []
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for episode in range(episodes):
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obs, info = env.reset(seed=seed + episode)
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start, goal = tuple(info["position"]), tuple(info["goal"])
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straight = math.dist(start, goal)
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steps = 0
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for _ in range(LOW_MAX_STEPS + 2):
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action, _state = model.predict(obs, deterministic=True)
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obs, _r, terminated, truncated, info = env.step(int(action))
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steps += 1
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if terminated or truncated:
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break
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successes += int(terminated)
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efficiencies.append(min(1.0, straight /
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max(info["path_length_m"], 1e-9)))
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env.close()
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return {"episodes": episodes,
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"success_rate": successes / episodes,
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"mean_path_efficiency": sum(efficiencies) / len(efficiencies)}
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def main(argv=None) -> int:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--timesteps", type=int, default=60_000)
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parser.add_argument("--seed", type=int, default=20260911)
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parser.add_argument("--bc-episodes", type=int, default=600)
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parser.add_argument("--bc-epochs", type=int, default=12)
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parser.add_argument("--envs", type=int, default=4)
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parser.add_argument("--rollout-steps", type=int, default=512,
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help="每环境每次 PPO rollout 长度；短 smoke 可设 8")
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parser.add_argument("--device", default="cpu")
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parser.add_argument("--model", default="output/hrl/hrl_low.zip")
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parser.add_argument("--eval-episodes", type=int, default=40)
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parser.add_argument("--skip-bc", action="store_true",
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help="跳过行为克隆（纯 PPO 对照实验用）")
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parser.add_argument("--verbose", type=int, default=1)
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args = parser.parse_args(argv)
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if min(args.envs, args.timesteps, args.eval_episodes,
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args.rollout_steps) <= 0:
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parser.error("envs/timesteps/eval-episodes/rollout-steps 必须为正数")
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if args.envs * args.rollout_steps < 2:
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parser.error("PPO rollout 至少需要 2 个样本")
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if not args.skip_bc and (args.bc_episodes < 2 or args.bc_epochs < 1):
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parser.error("BC 至少需要 2 局和 1 个 epoch")
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import torch
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torch.set_num_threads(1)
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from sb3_contrib import MaskablePPO
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from stable_baselines3.common.monitor import Monitor
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from stable_baselines3.common.vec_env import DummyVecEnv
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from stable_baselines3.common.utils import set_random_seed
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set_random_seed(args.seed)
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model_path = Path(args.model)
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model_path.parent.mkdir(parents=True, exist_ok=True)
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curve_paths = [model_path.with_name(
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f"{model_path.stem}_curve_env{rank}.monitor.csv")
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for rank in range(args.envs)]
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def _factory(rank: int):
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def _make():
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env = Monitor(LowLevelEnv(seed=args.seed + rank),
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filename=str(curve_paths[rank]))
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return env
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return _make
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vec_env = DummyVecEnv([_factory(rank) for rank in range(args.envs)])
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started = time.monotonic()
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bc_valid_acc = None
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if args.skip_bc:
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model = MaskablePPO("MlpPolicy", vec_env,
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policy_kwargs={"net_arch": list(HIDDEN)},
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n_steps=args.rollout_steps, batch_size=128, n_epochs=6,
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gamma=1.0, gae_lambda=0.95, learning_rate=3e-4,
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clip_range=0.2, ent_coef=0.005, vf_coef=0.5,
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max_grad_norm=0.5, seed=args.seed,
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device=args.device, verbose=args.verbose)
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else:
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x, y, episode_ids = collect(args.bc_episodes, args.seed, ProblemConfig())
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bc_model, bc_valid_acc = train_bc(
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x, y, episode_ids, epochs=args.bc_epochs, batch_size=256,
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lr=3e-4, device=args.device, seed=args.seed)
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model = copy_into_ppo(bc_model, args.device, vec_env, seed=args.seed,
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n_steps=args.rollout_steps)
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bc_dir = model_path.parent / "bc_low.pt"
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torch.save(bc_model.state_dict(), str(bc_dir))
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print(json.dumps({"event": "bc_done", "samples": int(len(x))},
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ensure_ascii=False))
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model.learn(total_timesteps=args.timesteps,
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progress_bar=args.verbose > 0)
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elapsed_s = time.monotonic() - started
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model.save(str(model_path))
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eval_seed = args.seed + 1_000_000
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evaluation = evaluate(model, episodes=args.eval_episodes, seed=eval_seed,
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config=ProblemConfig())
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report = {
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"level": "low", "timesteps": args.timesteps, "seed": args.seed,
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"device": args.device, "wall_seconds": round(elapsed_s, 1),
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"model": str(model_path), "algorithm": "maskable_ppo",
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"feature_version": FEATURE_VERSION, "bc_skipped": args.skip_bc,
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"bc_valid_acc": bc_valid_acc, "evaluation_seed": eval_seed,
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"monitor_files": [str(path) for path in curve_paths],
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"training_role": "standalone_navigation_only",
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"obs_dim": LOW_OBS_DIM, "act_dim": LOW_NUM_ACTIONS,
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"model_sha256": hashlib.sha256(model_path.read_bytes()).hexdigest(),
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**evaluation,
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}
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model_path.with_suffix(".json").write_text(
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json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
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print(json.dumps(report, ensure_ascii=False, indent=2))
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vec_env.close()
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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