diff --git a/agent/experiments/exp11b_parallel_v6.py b/agent/experiments/exp11b_parallel_v6.py new file mode 100644 index 0000000..e074cfb --- /dev/null +++ b/agent/experiments/exp11b_parallel_v6.py @@ -0,0 +1,201 @@ +""" +Exp 11b: Parallel DummyVecEnv — generated_track + mountain_track, v6 reward. + +Changes from Exp 11 (aborted): + - Reward v6: speed × CTE_quality + efficiency GATE (prevents circular driving) + - stuck_steps: 80 → 40 (faster termination when stuck against barriers) + - Everything else identical: same tracks, same hyperparameters, same parallel setup + +Setup: + - Sim 1: 10.0.0.55:9091 → generated_track + - Sim 2: 10.0.0.55:9093 → mountain_track + - DummyVecEnv wraps both → PPO sees both tracks in every rollout batch + - NO env closing, NO set_env(), NO track switching + +Hypothesis: v6 reward fixes circular driving exploit seen in Exp 11 while +preserving gradient signal on mountain_track hills. Parallel envs provide +stable multi-track learning (no catastrophic forgetting). +""" +import sys, os, time +sys.path.insert(0, '/home/paulh/projects/donkeycar-rl-autoresearch/agent') + +from multitrack_runner import log, StuckTerminationWrapper +from donkeycar_sb3_runner import ThrottleClampWrapper +from reward_wrapper import SpeedRewardWrapper +from stable_baselines3 import PPO +from stable_baselines3.common.vec_env import DummyVecEnv, VecTransposeImage +import gymnasium as gym +import numpy as np + +HOST = '10.0.0.55' +THROTTLE_MIN = 0.2 +LR = 0.000725 +TOTAL_STEPS = 90000 +SAVE_DIR = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/exp11b-parallel-v6' +os.makedirs(SAVE_DIR, exist_ok=True) + +def make_env(track_id, port): + def _init(): + raw = gym.make(track_id, conf={'host': HOST, 'port': port}) + env = ThrottleClampWrapper(raw, throttle_min=THROTTLE_MIN) + env = StuckTerminationWrapper(env, stuck_steps=40, min_displacement=0.5) + env = SpeedRewardWrapper(env) # v6: speed×CTE + efficiency gate + return env + return _init + +log('='*60) +log('Exp 11b: Parallel DummyVecEnv — v6 reward (anti-circle gate)') +log(f' Sim 1: {HOST}:9091 → generated_track') +log(f' Sim 2: {HOST}:9093 → mountain_track') +log(f' throttle_min={THROTTLE_MIN}, lr={LR}, total={TOTAL_STEPS:,}') +log(f' Reward: v6 (speed × CTE_quality, gated by efficiency >= 0.15)') +log(f' Stuck: 40 steps (~2.5s)') +log(f' Method: DummyVecEnv (both tracks in every PPO batch)') +log('='*60) + +# Create parallel env +log('Creating DummyVecEnv with two tracks...') +env = DummyVecEnv([ + make_env('donkey-generated-track-v0', 9091), + make_env('donkey-mountain-track-v0', 9093), +]) +env = VecTransposeImage(env) +log(f' VecEnv num_envs={env.num_envs}, obs={env.observation_space.shape}') + +# Create PPO +model = PPO('CnnPolicy', env, learning_rate=LR, verbose=1, device='cpu') +log('PPO created. Starting training...') + +# Train in segments for checkpointing +CHECKPOINT_EVERY = 6000 +best_reward = float('-inf') +steps_done = 0 + +while steps_done < TOTAL_STEPS: + seg_steps = min(CHECKPOINT_EVERY, TOTAL_STEPS - steps_done) + model.learn(total_timesteps=seg_steps, reset_num_timesteps=False) + steps_done += seg_steps + + # Save checkpoint + ckpt = os.path.join(SAVE_DIR, f'checkpoint_{steps_done:07d}') + model.save(ckpt) + model.save(os.path.join(SAVE_DIR, 'model')) # latest for crash recovery + log(f'[{steps_done:,}/{TOTAL_STEPS:,}] Checkpoint saved: {ckpt}.zip') + + # Quick eval on both tracks simultaneously + try: + obs = env.reset() + ep_rewards = np.zeros(env.num_envs) + ep_steps = np.zeros(env.num_envs) + done_mask = np.zeros(env.num_envs, dtype=bool) + for _ in range(2000): + action, _ = model.predict(obs, deterministic=True) + obs, rewards, dones, infos = env.step(action) + for i in range(env.num_envs): + if not done_mask[i]: + ep_rewards[i] += rewards[i] + ep_steps[i] += 1 + if dones[i]: + done_mask[i] = True + if done_mask.all(): + break + + status0 = '✅' if ep_steps[0] >= 2000 else f'❌@{int(ep_steps[0])}' + status1 = '✅' if ep_steps[1] >= 2000 else f'❌@{int(ep_steps[1])}' + log(f' Eval: gen_track={ep_rewards[0]:.1f}r/{int(ep_steps[0])}s {status0} ' + f'mountain={ep_rewards[1]:.1f}r/{int(ep_steps[1])}s {status1}') + + total_reward = ep_rewards.sum() + if total_reward > best_reward: + best_reward = total_reward + model.save(os.path.join(SAVE_DIR, 'best_model')) + log(f' ⭐ NEW BEST: {best_reward:.1f} (combined)') + except Exception as e: + log(f' Eval error: {e}') + import traceback; traceback.print_exc() + +model.save(os.path.join(SAVE_DIR, 'model')) +log(f'\nTraining complete. Best combined reward: {best_reward:.1f}') +log(f'Checkpoints in {SAVE_DIR}:') +for f in sorted(os.listdir(SAVE_DIR)): + size = os.path.getsize(os.path.join(SAVE_DIR, f)) // (1024*1024) + log(f' {f} ({size}MB)') + +# Close training env +env.close() +time.sleep(5) + +# --- Eval on all 4 tracks using sim 1 (port 9091) --- +# We use a single sim for sequential eval since we only need one track at a time +log('\n' + '='*60) +log('EVALUATION: best_model on 4 tracks (3 sets each)') +log('='*60) + +EVAL_TRACKS = [ + ('donkey-mountain-track-v0', 'mountain_track'), + ('donkey-generated-track-v0', 'generated_track'), + ('donkey-generated-roads-v0', 'generated_road'), + ('donkey-minimonaco-track-v0', 'mini_monaco'), +] +EVAL_SETS = 3 +EVAL_MAX_STEPS = 2000 +EVAL_PORT = 9091 + +best_model_path = os.path.join(SAVE_DIR, 'best_model.zip') +results_by_track = {} + +for track_id, track_name in EVAL_TRACKS: + log(f'\n--- {track_name} ---') + steps_list = [] + + for s in range(1, EVAL_SETS + 1): + try: + # Create fresh env for each eval run + raw = gym.make(track_id, conf={'host': HOST, 'port': EVAL_PORT}) + eval_env_inner = ThrottleClampWrapper(raw, throttle_min=THROTTLE_MIN) + eval_env_inner = StuckTerminationWrapper(eval_env_inner, stuck_steps=40, min_displacement=0.5) + eval_env_inner = SpeedRewardWrapper(eval_env_inner) + eval_env = VecTransposeImage(DummyVecEnv([lambda e=eval_env_inner: e])) + + eval_model = PPO.load(best_model_path, env=eval_env, device='cpu') + + obs = eval_env.reset() + total_r, total_s, done = 0.0, 0, False + while not done and total_s < EVAL_MAX_STEPS: + action, _ = eval_model.predict(obs, deterministic=True) + result = eval_env.step(action) + if len(result) == 4: + obs, r, d, info = result + done = bool(d[0]) + else: + obs, r, t, tr, info = result + done = bool(t[0] or tr[0]) + total_r += float(r[0]) + total_s += 1 + + status = '✅' if total_s >= EVAL_MAX_STEPS else f'❌@{total_s}' + log(f' Set{s}: {total_r:.1f}r / {total_s}s {status}') + steps_list.append(total_s) + + eval_env.close() + time.sleep(3) + except Exception as e: + log(f' Set{s}: ERROR — {e}') + steps_list.append(0) + time.sleep(3) + + mean_steps = np.mean(steps_list) if steps_list else 0 + results_by_track[track_name] = steps_list + log(f' Mean: {mean_steps:.0f} steps') + +# Summary +log('\n' + '='*60) +log('SUMMARY') +log('='*60) +for track_name, steps_list in results_by_track.items(): + steps_str = '/'.join(str(s) for s in steps_list) + mean = np.mean(steps_list) + verdict = '✅' if mean >= 1500 else '⚠️' if mean >= 500 else '❌' + log(f' {verdict} {track_name:20s}: {steps_str} mean={mean:.0f}') + +log(f'\n=== Exp 11b COMPLETE ===')