218 lines
7.7 KiB
Python
218 lines
7.7 KiB
Python
"""
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DQN训练脚本 - SUMO VSL环境
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"""
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import argparse
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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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from envs.edge_vsl_env import SUMOEdgeVSLEnvironment
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from agents.dqn_agent import DQNAgent
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from utils.config import get_agent_config, get_training_config
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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 add_run_dir_args, resolve_run_dirs
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def train_sumo_dqn(log_dir=None, checkpoint_dir=None, run_timestamp=None):
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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, "dqn")
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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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"dqn",
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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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with open(os.path.join(checkpoint_dir, "config.yaml"), "w", encoding="utf-8") as f:
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yaml.dump(runtime_config, f)
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logger = TrainingLogger(log_dir, "dqn")
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env = SUMOEdgeVSLEnvironment(runtime_config)
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state_dim = env.state_dim
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# DQN使用单个网络处理所有边
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num_edges = env.num_edges
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num_actions_per_edge = env.action_dim
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print("=" * 70)
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print("DQN训练 - SUMO 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: {num_edges}")
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print(f" 每边动作数: {num_actions_per_edge}")
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print(f" Episode步数: {env.episode_length}")
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print()
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# 创建单个DQN agent
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agent = DQNAgent(
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state_dim=state_dim,
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num_edges=num_edges,
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num_actions_per_edge=num_actions_per_edge,
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hidden_dim=agent_config.get("hidden_dim", 256),
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learning_rate=agent_config.get("learning_rate", 1e-3),
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gamma=agent_config.get("gamma", 0.99),
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epsilon_start=agent_config.get("epsilon_start", 1.0),
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epsilon_end=agent_config.get("epsilon_end", 0.01),
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epsilon_decay=agent_config.get("epsilon_decay", 200),
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buffer_size=agent_config.get("buffer_size", 10000),
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batch_size=agent_config.get("batch_size", 64),
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target_update=agent_config.get("target_update", 10),
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device=agent_config.get("device", "cuda")
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)
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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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episode_rewards = []
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episode_throughputs = []
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episode_mean_speeds = []
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episode_speed_stds = []
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episode_hard_brakes = []
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losses = []
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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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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_std = 0
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episode_r_flow = 0
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episode_r_var = 0
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episode_r_brake = 0
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episode_r_penalty = 0
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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(total=env.episode_length, desc=f"Ep {episode}/{num_episodes}", leave=False)
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while not done:
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# 单个agent选择所有边的动作
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action = agent.select_action(state)
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next_state, reward, done, info = env.step(action)
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# 存储转换并更新
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agent.store_transition(state, action, reward, next_state, done)
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train_stats = agent.update()
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if train_stats:
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losses.append(train_stats["loss"])
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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_std += info["speed_std"] * 3.6
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episode_r_flow += info["r_flow"]
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episode_r_var += info["r_var"]
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episode_r_brake += info["r_brake"]
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episode_r_penalty += info["r_penalty"]
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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(r=f"{episode_reward:.1f}", tp=f"{info['throughput']:.0f}",
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v=f"{info['mean_speed_kmh']:.1f}")
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pbar.update(1)
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pbar.close()
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if episode % agent.target_update == 0:
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agent.update_target_network()
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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_std = episode_speed_std / max(step, 1)
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avg_r_flow = episode_r_flow / max(step, 1)
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avg_r_var = episode_r_var / max(step, 1)
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avg_r_brake = episode_r_brake / max(step, 1)
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avg_r_penalty = episode_r_penalty / max(step, 1)
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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_stds.append(avg_speed_std)
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episode_hard_brakes.append(episode_brakes)
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loss_val = np.mean(losses[-100:]) if losses else None
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logger.log(
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episode, episode_reward, avg_tp, avg_speed,
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speed_std=avg_speed_std,
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r_flow=avg_r_flow,
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r_var=avg_r_var,
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r_brake=avg_r_brake,
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r_penalty=avg_r_penalty,
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hard_brakes=episode_brakes,
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value_loss=loss_val,
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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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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" Speed Std: {avg_speed_std:.2f} km/h")
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print(f" R(flow/var/brake/pen): {avg_r_flow:.3f} / {avg_r_var:.3f} / {avg_r_brake:.3f} / {avg_r_penalty:.3f}")
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if losses:
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print(f" Loss: {np.mean(losses[-100:]):.4f}")
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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")
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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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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_stds, episode_hard_brakes,
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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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if __name__ == "__main__":
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parser = add_run_dir_args(argparse.ArgumentParser())
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args = parser.parse_args()
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train_sumo_dqn(
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log_dir=args.log_dir,
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checkpoint_dir=args.checkpoint_dir,
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run_timestamp=args.run_timestamp,
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)
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