113 lines
5.4 KiB
Python
113 lines
5.4 KiB
Python
import argparse
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import gymnasium as gym
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import gym_donkeycar
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from stable_baselines3 import DQN, PPO
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from stable_baselines3.common.evaluation import evaluate_policy
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import os
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import sys
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import time
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from discretize_action import DiscretizedActionWrapper
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AGENT_MAP = {
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'dqn': DQN,
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'ppo': PPO, # For later extension
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}
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def run_training(env_id, agent_name, total_timesteps, reward_shaping=False, eval_episodes=10, log_dir=None, seed=None, dqn_discretize=True, n_steer=3, n_throttle=3):
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assert agent_name in AGENT_MAP, f"Agent '{agent_name}' not recognized. Available: {list(AGENT_MAP.keys())}"
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AgentClass = AGENT_MAP[agent_name]
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print('[SB3 Runner] Starting: Connecting to sim…', flush=True)
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start = time.time()
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try:
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env = gym.make(env_id)
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print(f'[SB3 Runner][MONITOR] Connected to gym env. {time.ctime()}', flush=True)
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except Exception as e:
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print(f'[SB3 Runner][MONITOR ALERT] Failed to connect to sim: {str(e)}', flush=True)
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sys.exit(100)
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if agent_name == 'dqn' and dqn_discretize:
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env = DiscretizedActionWrapper(env, n_steer=n_steer, n_throttle=n_throttle)
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print(f'[SB3 Runner][MONITOR] Action discretization: steer={n_steer}, throttle={n_throttle}. {time.ctime()}', flush=True)
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EPISODES = 10 # Number of full env.reset runs for this special test
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try:
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ep_rewards = []
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for episode in range(EPISODES):
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ep_reward = 0.0
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if seed is not None:
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obs = env.reset(seed=seed)
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else:
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obs = env.reset()
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print(f'[SB3 Runner][TEST] Episode {episode+1}/{EPISODES} - reset at {time.ctime()}', flush=True)
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done = False
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t = 0
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while not done:
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action = env.action_space.sample()
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result = env.step(action)
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if len(result) in (4, 5): # obs, reward, done, info or obs, reward, done, truncated, info
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if len(result) == 4:
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obs, reward, done, info = result
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else:
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obs, reward, done, truncated, info = result
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done = done or truncated
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else:
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print('[SB3 Runner][MONITOR] UNEXPECTED step() result shape!', flush=True)
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break
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ep_reward += reward
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t += 1
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if t % 10 == 0 or done:
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print(f'[SB3 Runner][TEST] Step {t} done={done} reward={reward} {time.ctime()}', flush=True)
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if done:
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print(f'[SB3 Runner][TEST] Episode {episode+1} ended after {t} steps, total_reward={ep_reward} at {time.ctime()}', flush=True)
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break
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ep_rewards.append(ep_reward)
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print(f'[SB3 Runner][TEST] All episode rewards: {ep_rewards}', flush=True)
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if len(ep_rewards) > 0:
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print(f'[SB3 Runner][TEST] mean_reward={sum(ep_rewards)/len(ep_rewards):.4f}', flush=True)
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except Exception as e:
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print(f'[SB3 Runner][MONITOR ALERT] Exception during episodes: {str(e)} {time.ctime()}', flush=True)
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sys.exit(102)
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# ---- NEW: Ensure teardown and sleep for race avoidance ----
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print(f'[SB3 Runner][MONITOR] Calling env.close() at {time.ctime()}', flush=True)
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try:
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env.close()
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print(f'[SB3 Runner][MONITOR] env.close() complete. {time.ctime()}', flush=True)
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except Exception as e:
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print(f'[SB3 Runner][MONITOR ALERT] Exception during env.close(): {str(e)} {time.ctime()}', flush=True)
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print(f'[SB3 Runner][MONITOR] Waiting 2s before process exit to avoid race. {time.ctime()}', flush=True)
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time.sleep(2)
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print(f'[SB3 Runner][MONITOR] Exiting RL runner at {time.ctime()}', flush=True)
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# Save if needed
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if log_dir:
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os.makedirs(log_dir, exist_ok=True)
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save_path = os.path.join(log_dir, f'{agent_name}_model')
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model.save(save_path)
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print(f"[SB3 Runner] Model saved to {save_path}")
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mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=eval_episodes, return_episode_rewards=False)
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print(f"[SB3 Runner] Eval episodes={eval_episodes}: mean_reward={mean_reward:.3f} std={std_reward:.3f}")
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return mean_reward, std_reward
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Train/Eval an RL agent on DonkeyCar Gym using SB3.")
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parser.add_argument('--agent', type=str, default='dqn', choices=AGENT_MAP.keys(), help='RL agent type')
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parser.add_argument('--env', type=str, default='donkey-generated-roads-v0', help='Gym/Gymnasium env ID')
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parser.add_argument('--timesteps', type=int, default=5000, help='Total training timesteps')
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parser.add_argument('--eval-episodes', type=int, default=10, help='Episodes for evaluation after training')
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parser.add_argument('--log-dir', type=str, default=None, help='Directory to save models')
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parser.add_argument('--seed', type=int, default=None, help='Random seed')
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parser.add_argument('--n-steer', type=int, default=3, help='Number of steer bins (DQN only)')
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parser.add_argument('--n-throttle', type=int, default=3, help='Number of throttle bins (DQN only)')
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args = parser.parse_args()
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run_training(
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env_id=args.env,
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agent_name=args.agent,
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total_timesteps=args.timesteps,
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eval_episodes=args.eval_episodes,
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log_dir=args.log_dir,
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seed=args.seed,
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n_steer=args.n_steer,
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n_throttle=args.n_throttle
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)
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