feat: Exp 11c — parallel DummyVecEnv + v6 reward, extended to 250k steps
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Paul Huliganga
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"""
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Exp 11c: Parallel DummyVecEnv, v6 reward, 250k steps (extended budget).
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Exp 11b showed parallel envs + v6 reward works (no circles, stable training)
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but plateaued at ~194 steps with only 90k total steps (45k per track).
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This run extends to 250k steps (125k per track) to see if the model can
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break through the plateau with more training budget.
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Everything else identical to Exp 11b.
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"""
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import sys, os, time
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sys.path.insert(0, '/home/paulh/projects/donkeycar-rl-autoresearch/agent')
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from multitrack_runner import log, StuckTerminationWrapper
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from donkeycar_sb3_runner import ThrottleClampWrapper
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from reward_wrapper import SpeedRewardWrapper
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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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import gymnasium as gym
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import numpy as np
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HOST = '10.0.0.55'
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THROTTLE_MIN = 0.2
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LR = 0.000725
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TOTAL_STEPS = 250000
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SAVE_DIR = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/exp11c-parallel-v6-250k'
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os.makedirs(SAVE_DIR, exist_ok=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(env, stuck_steps=40, min_displacement=0.5)
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env = SpeedRewardWrapper(env) # v6
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return env
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return _init
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log('='*60)
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log('Exp 11c: Parallel DummyVecEnv, v6 reward, 250k steps')
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log(f' Sim 1: {HOST}:9091 → generated_track')
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log(f' Sim 2: {HOST}:9093 → mountain_track')
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log(f' throttle_min={THROTTLE_MIN}, lr={LR}, total={TOTAL_STEPS:,}')
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log(f' Reward: v6 (speed × CTE_quality, gated by efficiency >= 0.15)')
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log(f' Budget: 250k steps = ~125k per track (2.8× more than Exp 11b)')
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log('='*60)
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env = DummyVecEnv([
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make_env('donkey-generated-track-v0', 9091),
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make_env('donkey-mountain-track-v0', 9093),
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])
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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('CnnPolicy', env, learning_rate=LR, verbose=1, device='cpu')
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log('PPO created. Starting training...')
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CHECKPOINT_EVERY = 10000
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best_reward = float('-inf')
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steps_done = 0
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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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log(f'[{steps_done:,}/{TOTAL_STEPS:,}] Checkpoint saved')
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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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status0 = '✅' if ep_steps[0] >= 2000 else f'❌@{int(ep_steps[0])}'
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status1 = '✅' if ep_steps[1] >= 2000 else f'❌@{int(ep_steps[1])}'
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log(f' Eval: gen_track={ep_rewards[0]:.1f}r/{int(ep_steps[0])}s {status0} '
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f'mountain={ep_rewards[1]:.1f}r/{int(ep_steps[1])}s {status1}')
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total_reward = ep_rewards.sum()
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if total_reward > best_reward:
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best_reward = total_reward
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model.save(os.path.join(SAVE_DIR, 'best_model'))
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log(f' ⭐ NEW BEST: {best_reward:.1f} (combined)')
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except Exception as e:
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log(f' Eval error: {e}')
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model.save(os.path.join(SAVE_DIR, 'model'))
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log(f'\nTraining complete. Best combined reward: {best_reward:.1f}')
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# Close training env
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env.close()
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time.sleep(5)
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# --- Eval on all 4 tracks ---
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log('\n' + '='*60)
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log('EVALUATION: best_model on 4 tracks (3 sets each)')
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log('='*60)
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EVAL_TRACKS = [
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('donkey-mountain-track-v0', 'mountain_track'),
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('donkey-generated-track-v0', 'generated_track'),
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('donkey-generated-roads-v0', 'generated_road'),
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('donkey-minimonaco-track-v0', 'mini_monaco'),
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]
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EVAL_PORT = 9091
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best_model_path = os.path.join(SAVE_DIR, 'best_model.zip')
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results_by_track = {}
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for track_id, track_name in EVAL_TRACKS:
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log(f'\n--- {track_name} ---')
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steps_list = []
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for s in range(1, 4):
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try:
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raw = gym.make(track_id, conf={'host': HOST, 'port': EVAL_PORT})
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eval_inner = ThrottleClampWrapper(raw, throttle_min=THROTTLE_MIN)
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eval_inner = StuckTerminationWrapper(eval_inner, stuck_steps=40, min_displacement=0.5)
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eval_inner = SpeedRewardWrapper(eval_inner)
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eval_env = VecTransposeImage(DummyVecEnv([lambda e=eval_inner: e]))
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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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total_r, total_s, done = 0.0, 0, False
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while not done and total_s < 2000:
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action, _ = eval_model.predict(obs, deterministic=True)
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result = eval_env.step(action)
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if len(result) == 4: obs, r, d, _ = result; done = bool(d[0])
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else: obs, r, t, tr, _ = result; done = bool(t[0] or tr[0])
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total_r += float(r[0]); total_s += 1
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status = '✅' if total_s >= 2000 else f'❌@{total_s}'
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log(f' Set{s}: {total_r:.1f}r / {total_s}s {status}')
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steps_list.append(total_s)
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eval_env.close(); time.sleep(3)
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except Exception as e:
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log(f' Set{s}: ERROR — {e}')
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steps_list.append(0); time.sleep(3)
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results_by_track[track_name] = steps_list
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log(f' Mean: {np.mean(steps_list):.0f} steps')
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log('\n' + '='*60)
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log('SUMMARY')
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log('='*60)
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for track_name, steps_list in results_by_track.items():
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steps_str = '/'.join(str(s) for s in steps_list)
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mean = np.mean(steps_list)
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verdict = '✅' if mean >= 1500 else '⚠️' if mean >= 500 else '❌'
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log(f' {verdict} {track_name:20s}: {steps_str} mean={mean:.0f}')
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log(f'\n=== Exp 11c COMPLETE ===')
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