feat: Exp 14 — mountain_track, v5 reward, lap-based stopping
v5 required for mountain hills (v4 gives zero gradient on hills - documented Exp 1). Same simple approach as Exp 13 which worked: single track, minimal wrappers, lap-based stopping. ThrottleClamp + V5Reward only.
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Paul Huliganga
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"""
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Exp 14: Single track — mountain_track, v5 reward, lap-based stopping.
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v5 reward (speed x CTE) is required for mountain_track hills:
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- v4 (base x efficiency x speed) gives ZERO gradient on hills
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(efficiency=0, speed=0, all terms=0 simultaneously → no learning signal)
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- v5 (speed x CTE_quality) has non-zero gradient on hills:
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reward = speed_norm x cte_quality → dR/dspeed > 0 always
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Model CAN learn to apply more throttle on the hill.
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Proved in Exp 9 (mountain only, v5, throttle_min=0.2 → 2000/2000 steps).
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Circle exploit risk on mountain_track is lower than generated_track:
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- Mountain track geometry doesn't have flat open circling areas
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- The hill itself prevents sustained circling
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- Exp 9 succeeded without circle detection
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Same approach as Exp 13 (which worked):
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- Single track, simple wrapper stack, lap-based stopping
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- throttle_min=0.2 (v5 gradient teaches model to self-select high throttle)
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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 donkeycar_sb3_runner import ThrottleClampWrapper
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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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from datetime import datetime
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HOST = '10.0.0.55'
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PORT = 9091
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TRACK_ID = 'donkey-mountain-track-v0'
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TRACK_NAME = 'mountain_track'
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THROTTLE_MIN = 0.2
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LR = 0.000725
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MAX_STEPS = 300000
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EVAL_EVERY = 5000
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LAP_STOP = 3
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SAVE_DIR = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/exp14-mountain-v5'
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os.makedirs(SAVE_DIR, exist_ok=True)
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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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# ---- v5 reward (speed x CTE_quality) ----
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class V5RewardWrapper(gym.Wrapper):
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"""
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v5 reward: speed_norm x cte_quality.
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Non-zero gradient on hills — model learns to apply throttle.
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Simple, no efficiency gate, no extra terminators.
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"""
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def __init__(self, env, max_cte=8.0, min_lap_time=5.0):
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super().__init__(env)
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self.max_cte = max_cte
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self.min_lap_time = min_lap_time
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self._last_lc = 0
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def reset(self, **kwargs):
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self._last_lc = 0
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return self.env.reset(**kwargs)
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def step(self, action):
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result = self.env.step(action)
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if len(result) == 5:
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obs, _r, terminated, truncated, info = result
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done = terminated or truncated
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else:
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obs, _r, done, info = result
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terminated, truncated = done, False
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reward, force_term = self._compute(done, info)
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if force_term:
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terminated = True
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if len(result) == 5:
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return obs, reward, terminated, truncated, info
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return obs, reward, terminated or truncated, info
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def _compute(self, done, info):
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if done:
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return -1.0, False
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# Short-lap exploit check
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try:
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lc = int(info.get('lap_count', 0) or 0)
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except (TypeError, ValueError):
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lc = self._last_lc
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if lc > self._last_lc:
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self._last_lc = lc
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try:
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lt = float(info.get('last_lap_time', 999) or 999)
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except (TypeError, ValueError):
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lt = 999
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if lt < self.min_lap_time:
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penalty = -10.0 * (self.min_lap_time / max(lt, 0.1))
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return penalty, True
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try:
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cte = float(info.get('cte', 0) or 0)
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except (TypeError, ValueError):
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cte = 0.0
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cte_quality = 1.0 - min(abs(cte) / self.max_cte, 1.0)
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try:
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speed = max(0.0, float(info.get('speed', 0) or 0))
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except (TypeError, ValueError):
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speed = 0.0
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speed_norm = min(speed / 10.0, 1.0)
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return cte_quality * speed_norm, False
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def make_env():
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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 = V5RewardWrapper(env)
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return env
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return _init
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log('='*60)
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log(f'Exp 14: {TRACK_NAME}, v5 reward')
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log(f' Host: {HOST}:{PORT}')
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log(f' throttle_min={THROTTLE_MIN}, lr={LR}')
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log(f' Reward: v5 (speed x CTE_quality) — non-zero gradient on hills')
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log(f' Wrappers: ThrottleClamp + V5Reward ONLY')
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log(f' Stop: eval every {EVAL_EVERY:,} steps, stop at {LAP_STOP} laps')
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log(f' Safety ceiling: {MAX_STEPS:,} steps')
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log('='*60)
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env = VecTransposeImage(DummyVecEnv([make_env()]))
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model = PPO('CnnPolicy', env, learning_rate=LR, verbose=1, device='cpu')
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log('PPO created. Training...')
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best_reward = float('-inf')
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best_laps = 0
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steps_done = 0
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while steps_done < MAX_STEPS:
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seg = min(EVAL_EVERY, MAX_STEPS - steps_done)
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model.learn(total_timesteps=seg, reset_num_timesteps=False)
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steps_done += seg
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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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try:
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obs = env.reset()
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ep_r = 0.0
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ep_s = 0
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laps = 0
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prev_lc = 0
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for _ in range(2000):
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action, _ = model.predict(obs, deterministic=True)
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obs, r, d, info = env.step(action)
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ep_r += float(r[0])
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ep_s += 1
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try:
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lc = int((info[0] if isinstance(info, (list,tuple)) else info)
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.get('lap_count', 0) or 0)
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if lc > prev_lc:
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laps = lc
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prev_lc = lc
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except Exception:
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pass
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if bool(d[0]):
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break
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status = '✅' if ep_s >= 2000 else f'❌@{ep_s}'
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log(f'[{steps_done:,}] reward={ep_r:.1f} steps={ep_s} laps={laps} {status}')
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if ep_r > best_reward:
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best_reward = ep_r
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model.save(os.path.join(SAVE_DIR, 'best_model'))
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log(f' ⭐ NEW BEST: {best_reward:.1f}')
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if laps > best_laps:
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best_laps = laps
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log(f' 🏆 BEST LAPS: {best_laps}')
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if laps >= LAP_STOP:
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log(f' 🎯 {laps} laps at {steps_done:,} steps — STOPPING')
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break
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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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env.close()
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time.sleep(3)
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log(f'\nDone. best_laps={best_laps} best_reward={best_reward:.1f}')
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log(f'Best model: {SAVE_DIR}/best_model.zip')
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log('=== Exp 14 COMPLETE ===')
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