237 lines
7.9 KiB
Python
237 lines
7.9 KiB
Python
"""
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Exp 23: Clean slate — generated_road, solid barriers, simple reward.
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What changed from exp22:
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- Single track: generated_road on port 9091 only (diagnose one track first)
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- Simulator now uses BoxCollider barriers + CCD on the car Rigidbody.
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The car physically cannot escape. No Python-side exploit patches needed.
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- Reward wrapper v7: speed × CTE_quality + efficiency gate + no-progress kill.
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Removed: CTE-patience termination, solid_hit detection, wedge detection,
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MAX_EPISODE_SECONDS hard cap.
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- StuckTerminationWrapper: max_episode_seconds raised to 120s (genuine safety
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net only — physics handles the actual containment).
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- No warm-start: fresh PPO weights. Previous warm-starts were trained under
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broken reward/barrier conditions and add more noise than signal.
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- Total steps: 200k (more room to learn with clean signal).
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"""
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import os
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import sys
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import time
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from datetime import datetime
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sys.path.insert(0, '/home/paulh/projects/donkeycar-rl-autoresearch/agent')
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import gymnasium as gym
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import numpy as np
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from stable_baselines3 import PPO
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from stable_baselines3.common.vec_env import DummyVecEnv, VecTransposeImage
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from donkeycar_sb3_runner import ThrottleClampWrapper
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from multitrack_runner import StuckTerminationWrapper
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from reward_wrapper import SpeedRewardWrapper
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HOST = 'localhost'
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THROTTLE_MIN = 0.2
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LR = 0.0003
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TOTAL_STEPS = 200_000
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CHECKPOINT_EVERY = 10_000
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SAVE_DIR = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/exp23-generated-road-clean'
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os.makedirs(SAVE_DIR, exist_ok=True)
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# Reward wrapper v7 params — clean and minimal
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EFFICIENCY_WINDOW = 30
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MIN_EFFICIENCY = 0.15
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MAX_CTE = 8.0
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MIN_LAP_TIME = 12.0
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PROGRESS_PATIENCE = 100 # steps without new waypoint → terminate
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# StuckTerminationWrapper — generous limit, physics does the real work now
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MAX_STUCK_SECONDS = 5.0
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MAX_EPISODE_SECONDS = 120.0 # safety net only
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def log(msg):
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print(f'[{datetime.now().strftime("%H:%M:%S")}] {msg}', flush=True)
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def make_env(track_id, port):
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def _init():
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raw = gym.make(track_id, conf={'host': HOST, 'port': port})
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env = ThrottleClampWrapper(raw, throttle_min=THROTTLE_MIN)
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env = StuckTerminationWrapper(
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env,
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stuck_steps=40,
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min_displacement=0.5,
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max_stuck_seconds=MAX_STUCK_SECONDS,
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max_episode_seconds=MAX_EPISODE_SECONDS,
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)
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env = SpeedRewardWrapper(
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env,
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window_size=EFFICIENCY_WINDOW,
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min_efficiency=MIN_EFFICIENCY,
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max_cte=MAX_CTE,
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min_lap_time=MIN_LAP_TIME,
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progress_patience=PROGRESS_PATIENCE,
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)
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return env
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return _init
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def make_eval_env(track_id, port):
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inner = make_env(track_id, port)()
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return VecTransposeImage(DummyVecEnv([lambda e=inner: e]))
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log('=' * 60)
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log('Exp 23: generated_road — clean barriers, clean reward')
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log(f' Sim: {HOST}:9091 -> generated_road')
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log(f' throttle_min={THROTTLE_MIN}, lr={LR}, total={TOTAL_STEPS:,}')
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log(f' Reward: v7 (speed×CTE, efficiency gate, no-progress kill)')
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log(f' Max stuck: {MAX_STUCK_SECONDS}s, episode cap: {MAX_EPISODE_SECONDS}s (safety net)')
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log(f' Progress patience: {PROGRESS_PATIENCE} steps')
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log(f' Checkpoints every {CHECKPOINT_EVERY:,} steps')
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log('=' * 60)
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log('Creating DummyVecEnv on generated_road...')
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env = DummyVecEnv([make_env('donkey-generated-roads-v0', 9091)])
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env = VecTransposeImage(env)
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log(f' VecEnv num_envs={env.num_envs}, obs={env.observation_space.shape}')
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model = PPO(
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'CnnPolicy',
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env,
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learning_rate=LR,
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n_steps=2048,
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batch_size=64,
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n_epochs=10,
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gamma=0.99,
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gae_lambda=0.95,
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clip_range=0.2,
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ent_coef=0.01,
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verbose=1,
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device='cpu',
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)
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# Write PID for external monitoring
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pid_path = os.path.join(SAVE_DIR, 'current.pid')
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with open(pid_path, 'w') as f:
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f.write(str(os.getpid()))
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log(f'Fresh PPO model created. Starting training...')
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best_total_steps = float('-inf')
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best_total_reward = float('-inf')
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steps_done = 0
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run_tag = datetime.now().strftime('%Y-%m-%d_%H%M%S') + '_clean'
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log_path = os.path.join(SAVE_DIR, f'run_{run_tag}.log')
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best_model_path = os.path.join(SAVE_DIR, 'best_model.zip')
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import logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(message)s',
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handlers=[logging.FileHandler(log_path), logging.StreamHandler(sys.stdout)],
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)
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file_log = logging.getLogger('exp23')
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def flog(msg):
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ts = datetime.now().strftime('%H:%M:%S')
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file_log.info(f'[{ts}] {msg}')
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flog('=' * 60)
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flog(f'Exp 23 started — PID {os.getpid()}')
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flog(f'Log: {log_path}')
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flog('=' * 60)
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while steps_done < TOTAL_STEPS:
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seg_steps = min(CHECKPOINT_EVERY, TOTAL_STEPS - steps_done)
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model.learn(total_timesteps=seg_steps, reset_num_timesteps=False)
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steps_done += seg_steps
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ckpt = os.path.join(SAVE_DIR, f'checkpoint_{steps_done:07d}')
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model.save(ckpt)
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model.save(os.path.join(SAVE_DIR, 'model'))
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flog(f'[{steps_done:,}/{TOTAL_STEPS:,}] Checkpoint saved: {ckpt}.zip')
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# Mid-training eval on generated_road
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try:
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obs = env.reset()
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ep_rewards = np.zeros(env.num_envs)
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ep_steps = np.zeros(env.num_envs)
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done_mask = np.zeros(env.num_envs, dtype=bool)
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for _ in range(2000):
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action, _ = model.predict(obs, deterministic=True)
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obs, rewards, dones, infos = env.step(action)
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for i in range(env.num_envs):
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if not done_mask[i]:
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ep_rewards[i] += rewards[i]
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ep_steps[i] += 1
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if dones[i]:
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done_mask[i] = True
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if done_mask.all():
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break
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total_steps_eval = int(ep_steps.sum())
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total_reward_eval = float(ep_rewards.sum())
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status = '✅' if ep_steps[0] >= 2000 else f'❌@{int(ep_steps[0])}'
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flog(f' Eval: gen_road={total_reward_eval:.1f}r/{int(ep_steps[0])}s {status}')
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if (total_steps_eval > best_total_steps
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or (total_steps_eval == best_total_steps
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and total_reward_eval > best_total_reward)):
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best_total_steps = total_steps_eval
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best_total_reward = total_reward_eval
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model.save(best_model_path)
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flog(f' NEW BEST: steps={best_total_steps} reward={best_total_reward:.1f}')
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except Exception as e:
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flog(f' Eval error: {e}')
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env.close()
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# ── Final evaluation ──────────────────────────────────────────────────────────
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flog('=' * 60)
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flog('FINAL EVALUATION: best_model on generated_road')
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flog('=' * 60)
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EVAL_SETS = 3
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EVAL_MAX_STEPS = 2000
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steps_list = []
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reward_list = []
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for s in range(1, EVAL_SETS + 1):
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try:
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eval_env = make_eval_env('donkey-generated-roads-v0', 9091)
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eval_model = PPO.load(best_model_path, env=eval_env, device='cpu')
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obs = eval_env.reset()
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done = False
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total_s = 0
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total_r = 0.0
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while not done and total_s < EVAL_MAX_STEPS:
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action, _ = eval_model.predict(obs, deterministic=True)
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result = eval_env.step(action)
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obs, r, done = result[0], result[1], result[2]
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if hasattr(done, '__len__'):
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done = bool(done[0])
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total_r += float(r) if not hasattr(r, '__len__') else float(r[0])
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total_s += 1
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status = '✅' if total_s >= EVAL_MAX_STEPS else f'❌@{total_s}'
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flog(f' Set {s}: {total_r:.1f}r / {total_s}s {status}')
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steps_list.append(total_s)
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reward_list.append(total_r)
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eval_env.close()
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except Exception as e:
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flog(f' Set {s} error: {e}')
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if steps_list:
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flog(f' Mean: {np.mean(steps_list):.0f} steps / {np.mean(reward_list):.1f} reward')
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flog('Exp 23 complete.')
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