CLEAN: Robust multi-episode RL runner, no legacy save/model logic; outer loop points to project dir runner.

agent/donkeycar_outer_loop.py

@@ -51,7 +51,7 @@ def run_sweep():
                 mlog.write(f"[MONITOR {time.ctime()}] Launching inner RL job for config {i+1} repeat {r+1}\n")
                 mlog.flush()
             cmd = [
-            'python3', '/home/paulh/.pi/agent/donkeycar_sb3_runner.py',
+            'python3', '/home/paulh/projects/donkeycar-rl-autoresearch/agent/donkeycar_sb3_runner.py',
             '--agent', 'dqn',
             '--env', 'donkey-generated-roads-v0',
             '--timesteps', str(params['timesteps']),

agent/donkeycar_sb3_runner.py

@@ -1,40 +1,42 @@
 import argparse
 import gymnasium as gym
 import gym_donkeycar
-from stable_baselines3 import DQN, PPO
+import argparse
-from stable_baselines3.common.evaluation import evaluate_policy
+import gymnasium as gym
-import os
+import gym_donkeycar
 import sys
 import time
 from discretize_action import DiscretizedActionWrapper
 
-AGENT_MAP = {
+if __name__ == "__main__":
-    'dqn': DQN,
+    parser = argparse.ArgumentParser(description="Run multi-episode RL test loop for DonkeyCar Gym. No model training/saving.")
-    'ppo': PPO,  # For later extension
+    parser.add_argument('--agent', type=str, default='dqn', help='RL agent type (only dqn supported in this runner)')
-}
+    parser.add_argument('--env', type=str, default='donkey-generated-roads-v0', help='Gym/Gymnasium env ID')
-
+    parser.add_argument('--timesteps', type=int, default=5000, help='Unused (for outer loop compatibility)')
-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):
+    parser.add_argument('--eval-episodes', type=int, default=10, help='Episodes for evaluation')
-    assert agent_name in AGENT_MAP, f"Agent '{agent_name}' not recognized. Available: {list(AGENT_MAP.keys())}"
+    parser.add_argument('--log-dir', type=str, default=None, help='Unused (kept for arg compatibility)')
-    AgentClass = AGENT_MAP[agent_name]
+    parser.add_argument('--seed', type=int, default=None, help='Optional seed')
+    parser.add_argument('--n-steer', type=int, default=3, help='Number of steer bins (DQN only)')
+    parser.add_argument('--n-throttle', type=int, default=3, help='Number of throttle bins (DQN only)')
+    args = parser.parse_args()
 
     print('[SB3 Runner] Starting: Connecting to sim…', flush=True)
-    start = time.time()
     try:
-        env = gym.make(env_id)
+        env = gym.make(args.env)
         print(f'[SB3 Runner][MONITOR] Connected to gym env. {time.ctime()}', flush=True)
     except Exception as e:
         print(f'[SB3 Runner][MONITOR ALERT] Failed to connect to sim: {str(e)}', flush=True)
         sys.exit(100)
-    if agent_name == 'dqn' and dqn_discretize:
+    if args.agent == 'dqn':
-        env = DiscretizedActionWrapper(env, n_steer=n_steer, n_throttle=n_throttle)
+        env = DiscretizedActionWrapper(env, n_steer=args.n_steer, n_throttle=args.n_throttle)
-        print(f'[SB3 Runner][MONITOR] Action discretization: steer={n_steer}, throttle={n_throttle}. {time.ctime()}', flush=True)
+        print(f'[SB3 Runner][MONITOR] Action discretization: steer={args.n_steer}, throttle={args.n_throttle}. {time.ctime()}', flush=True)
-    EPISODES = 10  # Number of full env.reset runs for this special test
+    EPISODES = args.eval_episodes
     try:
         ep_rewards = []
         for episode in range(EPISODES):
             ep_reward = 0.0
-            if seed is not None:
+            if args.seed is not None:
-                obs = env.reset(seed=seed)
+                obs = env.reset(seed=args.seed)
             else:
                 obs = env.reset()
             print(f'[SB3 Runner][TEST] Episode {episode+1}/{EPISODES} - reset at {time.ctime()}', flush=True)
@@ -43,7 +45,7 @@ def run_training(env_id, agent_name, total_timesteps, reward_shaping=False, eval
             while not done:
                 action = env.action_space.sample()
                 result = env.step(action)
-                if len(result) in (4, 5):  # obs, reward, done, info or obs, reward, done, truncated, info
+                if len(result) in (4, 5):
                     if len(result) == 4:
                         obs, reward, done, info = result
                     else:
@@ -66,7 +68,6 @@ def run_training(env_id, agent_name, total_timesteps, reward_shaping=False, eval
     except Exception as e:
         print(f'[SB3 Runner][MONITOR ALERT] Exception during episodes: {str(e)} {time.ctime()}', flush=True)
         sys.exit(102)
-    # ---- NEW: Ensure teardown and sleep for race avoidance ----
     print(f'[SB3 Runner][MONITOR] Calling env.close() at {time.ctime()}', flush=True)
     try:
         env.close()
@@ -76,37 +77,3 @@ def run_training(env_id, agent_name, total_timesteps, reward_shaping=False, eval
     print(f'[SB3 Runner][MONITOR] Waiting 2s before process exit to avoid race. {time.ctime()}', flush=True)
     time.sleep(2)
     print(f'[SB3 Runner][MONITOR] Exiting RL runner at {time.ctime()}', flush=True)
-
-    # Save if needed
-    if log_dir:
-        os.makedirs(log_dir, exist_ok=True)
-        save_path = os.path.join(log_dir, f'{agent_name}_model')
-        model.save(save_path)
-        print(f"[SB3 Runner] Model saved to {save_path}")
-
-    mean_reward, std_reward = evaluate_policy(model, env, n_eval_episodes=eval_episodes, return_episode_rewards=False)
-    print(f"[SB3 Runner] Eval episodes={eval_episodes}: mean_reward={mean_reward:.3f} std={std_reward:.3f}")
-    return mean_reward, std_reward
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Train/Eval an RL agent on DonkeyCar Gym using SB3.")
-    parser.add_argument('--agent', type=str, default='dqn', choices=AGENT_MAP.keys(), help='RL agent type')
-    parser.add_argument('--env', type=str, default='donkey-generated-roads-v0', help='Gym/Gymnasium env ID')
-    parser.add_argument('--timesteps', type=int, default=5000, help='Total training timesteps')
-    parser.add_argument('--eval-episodes', type=int, default=10, help='Episodes for evaluation after training')
-    parser.add_argument('--log-dir', type=str, default=None, help='Directory to save models')
-    parser.add_argument('--seed', type=int, default=None, help='Random seed')
-    parser.add_argument('--n-steer', type=int, default=3, help='Number of steer bins (DQN only)')
-    parser.add_argument('--n-throttle', type=int, default=3, help='Number of throttle bins (DQN only)')
-    args = parser.parse_args()
-
-    run_training(
-        env_id=args.env,
-        agent_name=args.agent,
-        total_timesteps=args.timesteps,
-        eval_episodes=args.eval_episodes,
-        log_dir=args.log_dir,
-        seed=args.seed,
-        n_steer=args.n_steer,
-        n_throttle=args.n_throttle
-    )