285 lines
12 KiB
Python
285 lines
12 KiB
Python
"""GRPO-inspired PPO training entrypoint for corridor VSL control."""
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import os
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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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from tqdm import tqdm
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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.gpro_agent import GPROAgent
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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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from utils.seeding import derive_seed, resolve_base_seed, set_global_seed
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def train_sumo_gpro(log_dir=None, checkpoint_dir=None, run_timestamp=None):
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"""Train grouped relative PPO under the SUMO+TraCI VSL environment."""
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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, "gpro")
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train_config = get_training_config(config)
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base_seed = resolve_base_seed(train_config)
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set_global_seed(base_seed)
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_, checkpoint_dir, log_dir = resolve_run_dirs(
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"gpro",
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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, "gpro")
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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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group_size = int(agent_config.get("group_size", 4))
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print("=" * 70)
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print("GPRO-PPO training - SUMO+TraCI VSL environment")
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print("=" * 70)
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print(f" State dim: {state_dim}")
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print(f" Controlled edges: {env.num_edges}")
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print(f" Actions per edge: {env.action_dim}")
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print(f" Episode steps: {env.episode_length}")
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print(f" Control interval: {env.control_interval}s")
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print(f" Hidden layers: {agent_config.get('hidden_layers', [256, 256])}")
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print(f" Learning rate: {agent_config.get('learning_rate', 3e-4)}")
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print(f" Group size: {group_size}")
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print(f" Group advantage coef: {agent_config.get('group_advantage_coef', 0.35)}")
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print(f" Device: {agent_config.get('device', 'cuda')}")
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print()
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agent = GPROAgent(
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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", [256, 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", 0.01),
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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", 4),
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minibatch_size=agent_config.get("batch_size", 64),
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group_size=group_size,
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group_advantage_coef=agent_config.get("group_advantage_coef", 0.35),
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advantage_epsilon=agent_config.get("advantage_epsilon", 1e-8),
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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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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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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_ttc_risks = []
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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("Starting training...\n")
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try:
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pending_log_rows = []
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for group_start in range(1, num_episodes + 1, group_size):
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group_seed = derive_seed(base_seed, ((group_start - 1) // group_size) + 1)
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group_end = min(group_start + group_size - 1, num_episodes)
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pending_log_rows.clear()
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for episode in range(group_start, group_end + 1):
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state = env.reset(seed=group_seed)
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episode_reward = 0.0
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episode_throughput = 0.0
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episode_speed = 0.0
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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_ttc_risk = 0.0
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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_ttc_risk += float(info.get("ttc_risk_rate", 0.0))
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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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agent.finish_episode(episode_reward)
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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_ttc_risks.append(episode_ttc_risk)
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pending_log_rows.append(
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{
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"episode": episode,
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"reward": episode_reward,
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"avg_tp": avg_tp,
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"avg_speed": avg_speed,
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"avg_speed_variance_norm": avg_speed_variance_norm,
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"reward_components": dict(avg_reward_components),
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"episode_ttc_risk": episode_ttc_risk,
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}
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)
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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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"ttc_risk": float(episode_ttc_risk),
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"group_seed": int(group_seed),
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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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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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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 episode % save_freq == 0:
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agent.save(os.path.join(checkpoint_dir, f"model_ep{episode}.pt"))
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train_stats = agent.update()
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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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print(
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f"\nGroup update episodes {group_start}-{group_end} | "
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f"seed={group_seed} | "
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f"policy_loss={train_stats['policy_loss']:.4f} | "
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f"entropy={train_stats['entropy']:.4f} | "
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f"group_score_std={train_stats['group_score_std']:.4f}"
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)
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for row in pending_log_rows:
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if train_stats:
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logger.log(
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row["episode"],
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row["reward"],
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row["avg_tp"],
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row["avg_speed"],
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speed_variance_norm=row["avg_speed_variance_norm"],
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reward_components=row["reward_components"],
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ttc_risk=row["episode_ttc_risk"],
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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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row["episode"],
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row["reward"],
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row["avg_tp"],
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row["avg_speed"],
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speed_variance_norm=row["avg_speed_variance_norm"],
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reward_components=row["reward_components"],
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ttc_risk=row["episode_ttc_risk"],
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)
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except KeyboardInterrupt:
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print("\nTraining interrupted, saving current model...")
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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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plot_training_curves(
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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_ttc_risks,
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policy_losses,
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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("Training complete")
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print(f" Best reward: {best_reward:.2f}")
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print(f" Checkpoints: {checkpoint_dir}")
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print(f" Logs: {log_dir}")
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print("=" * 70)
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