276 lines
10 KiB
Python
276 lines
10 KiB
Python
"""
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基于 SUMO+TraCI 的 PPO 训练脚本
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使用微观仿真环境训练 VSL 控制策略
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"""
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import os
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import sys
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import copy
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import yaml
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import numpy as np
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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from datetime import datetime
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from tqdm import tqdm
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import torch
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from envs.edge_vsl_env import SUMOEdgeVSLEnvironment
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from envs.reward_system import REWARD_COMPONENT_COLUMNS, average_reward_components, init_reward_component_totals
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from agents.ppo_agent import PPOAgent
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from utils.config import get_agent_config, get_training_config
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from utils.episode_artifacts import save_training_episode_artifacts
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from utils.logger import TrainingLogger
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from utils.plot import plot_training_curves
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from utils.run_dirs import resolve_run_dirs, write_shared_run_config
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def train_sumo_ppo(log_dir=None, checkpoint_dir=None, run_timestamp=None):
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"""SUMO 环境下的 PPO 训练主函数"""
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# 加载配置
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with open("config_sumo_vsl.yaml", "r", encoding="utf-8") as f:
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config = yaml.safe_load(f)
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agent_config = get_agent_config(config, "ppo")
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train_config = get_training_config(config)
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start_episode = 1
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_, checkpoint_dir, log_dir = resolve_run_dirs(
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"ppo",
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log_dir=log_dir,
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checkpoint_dir=checkpoint_dir,
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run_timestamp=run_timestamp,
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)
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os.makedirs(checkpoint_dir, exist_ok=True)
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os.makedirs(log_dir, exist_ok=True)
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runtime_config = copy.deepcopy(config)
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runtime_config.setdefault("runtime", {})["output_dir"] = log_dir
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runtime_config["runtime"]["evaluation_mode"] = False
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write_shared_run_config(
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runtime_config,
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log_dir=log_dir,
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checkpoint_dir=checkpoint_dir,
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run_timestamp=run_timestamp,
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)
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logger = TrainingLogger(log_dir, "ppo")
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# 创建环境
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env = SUMOEdgeVSLEnvironment(runtime_config)
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state_dim = env.state_dim
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action_dims = [env.action_dim] * env.num_controlled_edges
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print("=" * 70)
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print("PPO 训练 - SUMO+TraCI VSL 环境")
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print("=" * 70)
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print(f" 状态维度: {state_dim}")
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print(f" 控制边数: {env.num_edges}")
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print(f" 每边动作数: {env.action_dim}")
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print(f" Episode 步数: {env.episode_length}")
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print(f" 控制间隔: {env.control_interval}s")
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print(f" 隐藏层: {agent_config.get('hidden_layers', [512, 256])}")
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print(f" 学习率: {agent_config.get('learning_rate', 3e-4)}")
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print(f" 设备: {agent_config.get('device', 'cuda')}")
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print()
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# 创建 PPO 智能体
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agent = PPOAgent(
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state_dim=state_dim,
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action_dims=action_dims,
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hidden_layers=agent_config.get("hidden_layers", [512, 256]),
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learning_rate=agent_config.get("learning_rate", 3e-4),
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gamma=agent_config.get("gamma", 0.99),
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gae_lambda=agent_config.get("gae_lambda", 0.95),
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clip_epsilon=agent_config.get("clip_epsilon", 0.2),
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value_coef=agent_config.get("value_coef", 0.5),
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entropy_coef=agent_config.get("entropy_coef", 0.02),
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max_grad_norm=agent_config.get("max_grad_norm", 0.5),
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ppo_epochs=agent_config.get("ppo_epochs", 10),
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minibatch_size=agent_config.get("batch_size", 64),
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device=agent_config.get("device", "cuda"),
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lr_schedule=agent_config.get("lr_schedule", "cosine"),
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total_episodes=train_config["num_episodes"],
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)
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# 训练参数
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num_episodes = train_config["num_episodes"]
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save_freq = train_config.get("save_freq", 50)
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log_freq = train_config.get("log_freq", 10)
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base_seed = train_config.get("random_seed", 42)
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# 统计变量
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episode_rewards = []
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episode_throughputs = []
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episode_mean_speeds = []
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episode_speed_variance_norms = []
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episode_hard_brakes = []
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policy_losses = []
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value_losses = []
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entropies = []
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best_reward = -float("inf")
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print("开始训练...\n")
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try:
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for episode in range(start_episode, num_episodes + 1):
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# 每个 episode 使用不同 seed 引入随机性
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seed = base_seed + episode
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state = env.reset(seed=seed)
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episode_reward = 0
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episode_throughput = 0
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episode_speed = 0
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episode_speed_variance_norm = 0.0
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episode_reward_components = init_reward_component_totals()
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episode_brakes = 0
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done = False
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step = 0
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pbar = tqdm(
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total=env.episode_length,
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desc=f"Ep {episode}/{num_episodes}",
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leave=False,
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)
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while not done:
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action, log_prob, value = agent.select_action(state, deterministic=False)
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next_state, reward, done, info = env.step(action)
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agent.store_transition(state, action, reward, value, log_prob, done)
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episode_reward += reward
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episode_throughput += info["throughput"]
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episode_speed += info["mean_speed_kmh"]
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episode_speed_variance_norm += info["speed_variance_norm"]
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for column in REWARD_COMPONENT_COLUMNS:
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episode_reward_components[column] += float(info.get(column, 0.0))
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episode_brakes += info["num_hard_brakes"]
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state = next_state
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step += 1
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pbar.set_postfix(
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r=f"{episode_reward:.1f}",
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tp=f"{info['throughput']:.0f}",
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v=f"{info['mean_speed_kmh']:.1f}",
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)
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pbar.update(1)
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pbar.close()
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# GAE 计算和策略更新
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if done:
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next_value = 0.0
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else:
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with torch.no_grad():
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next_state_tensor = torch.FloatTensor(next_state).unsqueeze(0).to(agent.device)
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next_value = agent.policy.get_value(next_state_tensor).item()
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train_stats = agent.update(next_value)
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# 记录统计
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avg_tp = episode_throughput / max(step, 1)
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avg_speed = episode_speed / max(step, 1)
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avg_speed_variance_norm = episode_speed_variance_norm / max(step, 1)
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avg_reward_components = average_reward_components(episode_reward_components, step)
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episode_rewards.append(episode_reward)
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episode_throughputs.append(avg_tp)
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episode_mean_speeds.append(avg_speed)
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episode_speed_variance_norms.append(avg_speed_variance_norm)
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episode_hard_brakes.append(episode_brakes)
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if train_stats:
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policy_losses.append(train_stats["policy_loss"])
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value_losses.append(train_stats["value_loss"])
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entropies.append(train_stats["entropy"])
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logger.log(
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episode, episode_reward, avg_tp, avg_speed,
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speed_variance_norm=avg_speed_variance_norm,
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reward_components=avg_reward_components,
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hard_brakes=episode_brakes,
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policy_loss=train_stats["policy_loss"],
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value_loss=train_stats["value_loss"],
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entropy=train_stats["entropy"],
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)
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else:
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logger.log(
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episode, episode_reward, avg_tp, avg_speed,
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speed_variance_norm=avg_speed_variance_norm,
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reward_components=avg_reward_components,
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hard_brakes=episode_brakes,
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)
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# 保存最佳模型
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episode_summary = {
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"episode": episode,
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"reward": float(episode_reward),
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"avg_throughput": float(avg_tp),
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"avg_mean_speed_kmh": float(avg_speed),
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"avg_speed_variance_norm": float(avg_speed_variance_norm),
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"hard_brakes": int(episode_brakes),
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}
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for column, value in avg_reward_components.items():
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episode_summary[f"avg_{column}"] = float(value)
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if train_stats:
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episode_summary.update(
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policy_loss=float(train_stats["policy_loss"]),
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value_loss=float(train_stats["value_loss"]),
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entropy=float(train_stats["entropy"]),
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)
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save_training_episode_artifacts(
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log_dir=log_dir,
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episode=episode,
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episode_metrics=env.episode_metrics,
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control_edges=env.control_edges,
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summary=episode_summary,
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)
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if episode_reward > best_reward:
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best_reward = episode_reward
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agent.save(os.path.join(checkpoint_dir, "model_best.pt"))
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# 定期日志
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if episode % log_freq == 0:
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recent_rewards = episode_rewards[-log_freq:]
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print(f"\nEpisode {episode}/{num_episodes}")
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print(f" Reward: {episode_reward:.2f} (Avg: {np.mean(recent_rewards):.2f})")
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print(f" Throughput: {avg_tp:.1f} veh/h")
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print(f" Mean Speed: {avg_speed:.1f} km/h")
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print(f" Normalized Speed Variance: {avg_speed_variance_norm:.4f}")
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print(
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" Reward Components: "
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+ ", ".join(
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f"{column}={avg_reward_components[column]:.3f}"
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for column in REWARD_COMPONENT_COLUMNS
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)
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)
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if train_stats:
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print(f" Policy Loss: {train_stats['policy_loss']:.4f}")
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print(f" Value Loss: {train_stats['value_loss']:.4f}")
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print(f" Entropy: {train_stats['entropy']:.4f}")
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# 定期保存
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if episode % save_freq == 0:
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agent.save(os.path.join(checkpoint_dir, f"model_ep{episode}.pt"))
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except KeyboardInterrupt:
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print("\n训练被中断，保存当前模型...")
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agent.save(os.path.join(checkpoint_dir, "model_interrupted.pt"))
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finally:
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env.close()
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# 最终保存
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agent.save(os.path.join(checkpoint_dir, f"model_ep{num_episodes}.pt"))
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# 绘制训练曲线
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plot_training_curves(
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episode_rewards, episode_throughputs, episode_mean_speeds, episode_speed_variance_norms, episode_hard_brakes,
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policy_losses, value_losses,
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save_path=os.path.join(log_dir, "training_curves.png"),
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)
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print("=" * 70)
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print("训练完成！")
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print(f" 最佳奖励: {best_reward:.2f}")
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print(f" 模型目录: {checkpoint_dir}")
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print(f" 日志目录: {log_dir}")
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print("=" * 70)
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