fix: exp18 — fix circular exploit in parallel training (window=200, min_lap=12s)
Exp 17 post-mortem: efficiency gate window=30 steps only covers ~40% of a 3.5s exploit circle at 22fps, giving partial-arc efficiency ~0.77 (gate fires at 0.15). Car earned positive reward while circling, outweighing the -10 lap penalty. Performance peaked at 80k then collapsed. Exp 18 fixes: - window_size 30→200: covers 2+ full exploit circles, driving efficiency→0 - min_lap_time 5s→12s: genuine laps are 13-16s (gentrack) and 27-29s (mountain); anything under 12s is an exploit and terminates immediately Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Paul Huliganga
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@ -5,7 +5,8 @@ Each corresponds to an entry in docs/TEST_HISTORY.md.
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| Script | Experiment | Key change |
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| exp17_parallel_450k.py | Exp 17 | Parallel DummyVecEnv, 450k steps, v6 reward, HOST=localhost |
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| exp18_parallel_450k_v2.py | Exp 18 | Exp 17 + exploit fix: window=200, min_lap_time=12s |
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| exp17_parallel_450k.py | Exp 17 | Parallel DummyVecEnv, 450k steps, v6 reward — circular exploit dominated |
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| mountain_v5.py | Exp 5 | v5 reward + throttle_min=0.5, direct model.learn() |
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| mountain_continue.py | Exp 4 | Continued Exp3 training |
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| mountain_high_throttle.py | Exp 3 | throttle_min=0.5, old v4 reward |
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@ -0,0 +1,201 @@
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"""
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Exp 18: Parallel DummyVecEnv — 450k steps, fixed efficiency gate (v2).
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Problem with Exp 17: circular exploit dominated entire run.
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Root cause: v6 efficiency gate window=30 steps (~1.4s at 22fps).
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Exploit circles complete in ~77 steps (3.5s). In a 30-step window the car
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covers only ~40% of one circle — partial arc efficiency ≈ 0.77, well above
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the 0.15 threshold. Gate never fired. Car earned positive reward while
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circling, outweighing the lap-termination penalty.
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Fixes:
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1. window_size: 30 → 200 steps (~9s). Now covers 2+ full exploit circles,
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driving efficiency → 0 and reliably triggering the gate.
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2. min_lap_time: 5.0s → 12.0s. Genuine generated_track laps are 13-16s;
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genuine mountain laps are 27-29s. Anything under 12s is an exploit.
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Episode terminates immediately with large penalty on any sub-12s lap.
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Everything else identical to Exp 17.
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Setup — TWO sim instances required:
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Sim 1: donkey_sim.exe on port 9091 → generated_track
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Sim 2: separate copy of donkey_sim.exe on port 9093 → mountain_track
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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 = 'localhost'
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THROTTLE_MIN = 0.2
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LR = 0.000725
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TOTAL_STEPS = 450_000
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CHECKPOINT_EVERY = 20_000
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SAVE_DIR = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/exp18-parallel-450k-v2'
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os.makedirs(SAVE_DIR, exist_ok=True)
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# Exploit fixes: larger window catches full circles; higher min_lap_time
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# kills sub-genuine laps before they can contribute positive reward.
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EFFICIENCY_WINDOW = 200 # was 30 — now covers 2+ exploit circles at 22fps
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MIN_LAP_TIME = 12.0 # was 5.0 — genuine laps: gentrack 13-16s, mountain 27-29s
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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, window_size=EFFICIENCY_WINDOW, min_lap_time=MIN_LAP_TIME)
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return env
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return _init
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log('=' * 60)
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log('Exp 18: Parallel DummyVecEnv — 450k steps (exploit fix v2)')
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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 + exploit fix (window={EFFICIENCY_WINDOW}, min_lap={MIN_LAP_TIME}s)')
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log(f' Stuck termination: 40 steps (~2.5s)')
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log(f' Checkpoints: every {CHECKPOINT_EVERY:,} steps')
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log('=' * 60)
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log('Creating DummyVecEnv with two tracks...')
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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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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: {ckpt}.zip')
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# Eval on both training tracks using the existing DummyVecEnv connections
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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 reward')
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except Exception as e:
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log(f' Eval error: {e}')
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import traceback; traceback.print_exc()
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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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env.close()
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time.sleep(5)
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# --- Final eval on all 4 tracks (sequential, port 9091) ---
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log('\n' + '=' * 60)
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log('FINAL 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-generated-track-v0', 'generated_track'),
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('donkey-mountain-track-v0', 'mountain_track'),
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('donkey-minimonaco-track-v0', 'mini_monaco'),
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('donkey-generated-roads-v0', 'generated_road'),
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]
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EVAL_PORT = 9091
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EVAL_SETS = 3
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EVAL_MAX_STEPS = 2000
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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, EVAL_SETS + 1):
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try:
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raw = gym.make(track_id, conf={'host': HOST, 'port': EVAL_PORT})
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inner = ThrottleClampWrapper(raw, throttle_min=THROTTLE_MIN)
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inner = StuckTerminationWrapper(inner, stuck_steps=40, min_displacement=0.5)
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inner = SpeedRewardWrapper(inner)
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eval_env = VecTransposeImage(DummyVecEnv([lambda e=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 < 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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if len(result) == 4:
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obs, r, d, info = result
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done = bool(d[0])
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else:
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obs, r, t, tr, info = result
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done = bool(t[0] or tr[0])
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total_r += 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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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()
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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)
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time.sleep(3)
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mean_steps = np.mean(steps_list) if steps_list else 0
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results_by_track[track_name] = steps_list
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log(f' Mean: {mean_steps:.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 17 COMPLETE ===')
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