CLEAN: Robust multi-episode RL runner, no legacy save/model logic; outer loop points to project dir runner.
This commit is contained in:
Paul Huliganga
parent
c98bc7ef38
commit
4a4e61d463
|
|
@ -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']),
|
||||
|
|
|
|||
|
|
@ -1,40 +1,42 @@
|
|||
import argparse
|
||||
import gymnasium as gym
|
||||
import gym_donkeycar
|
||||
from stable_baselines3 import DQN, PPO
|
||||
from stable_baselines3.common.evaluation import evaluate_policy
|
||||
import os
|
||||
import argparse
|
||||
import gymnasium as gym
|
||||
import gym_donkeycar
|
||||
import sys
|
||||
import time
|
||||
from discretize_action import DiscretizedActionWrapper
|
||||
|
||||
AGENT_MAP = {
|
||||
'dqn': DQN,
|
||||
'ppo': PPO, # For later extension
|
||||
}
|
||||
|
||||
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):
|
||||
assert agent_name in AGENT_MAP, f"Agent '{agent_name}' not recognized. Available: {list(AGENT_MAP.keys())}"
|
||||
AgentClass = AGENT_MAP[agent_name]
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Run multi-episode RL test loop for DonkeyCar Gym. No model training/saving.")
|
||||
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)')
|
||||
parser.add_argument('--eval-episodes', type=int, default=10, help='Episodes for evaluation')
|
||||
parser.add_argument('--log-dir', type=str, default=None, help='Unused (kept for arg compatibility)')
|
||||
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:
|
||||
env = DiscretizedActionWrapper(env, n_steer=n_steer, n_throttle=n_throttle)
|
||||
print(f'[SB3 Runner][MONITOR] Action discretization: steer={n_steer}, throttle={n_throttle}. {time.ctime()}', flush=True)
|
||||
EPISODES = 10 # Number of full env.reset runs for this special test
|
||||
if args.agent == 'dqn':
|
||||
env = DiscretizedActionWrapper(env, n_steer=args.n_steer, n_throttle=args.n_throttle)
|
||||
print(f'[SB3 Runner][MONITOR] Action discretization: steer={args.n_steer}, throttle={args.n_throttle}. {time.ctime()}', flush=True)
|
||||
EPISODES = args.eval_episodes
|
||||
try:
|
||||
ep_rewards = []
|
||||
for episode in range(EPISODES):
|
||||
ep_reward = 0.0
|
||||
if seed is not None:
|
||||
obs = env.reset(seed=seed)
|
||||
if args.seed is not None:
|
||||
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
|
||||
)
|
||||
|
|
|
|||
Loading…
Reference in New Issue