87 lines
3.8 KiB
Python
87 lines
3.8 KiB
Python
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, _send_exit_scene
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from donkeycar_sb3_runner import ThrottleClampWrapper
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from reward_wrapper import SpeedRewardWrapper
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from multitrack_runner import StuckTerminationWrapper
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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 stable_baselines3.common.callbacks import BaseCallback
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import gymnasium as gym
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THROTTLE_MIN = 0.5
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EXTRA_STEPS = 110000 # already did 90k, adding 110k = 200k total
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BASE_MODEL = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/exp3-mountain-throttle05/model.zip'
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SAVE_PATH = '/home/paulh/projects/donkeycar-rl-autoresearch/agent/models/exp4-mountain-200k/model'
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os.makedirs(os.path.dirname(SAVE_PATH), exist_ok=True)
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def make_env(env_id):
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raw = gym.make(env_id)
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env = ThrottleClampWrapper(raw, throttle_min=THROTTLE_MIN)
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env = StuckTerminationWrapper(env, stuck_steps=80, min_displacement=0.5)
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env = SpeedRewardWrapper(env, speed_scale=0.1)
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return env
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def switch_to(current_id, next_id, name):
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log(f' → Switching to {name}...')
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tmp = gym.make(current_id); time.sleep(2)
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_send_exit_scene(tmp, verbose=False); tmp.close(); time.sleep(5)
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raw = gym.make(next_id)
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env = VecTransposeImage(DummyVecEnv([lambda: make_env(next_id)]))
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log(f' Connected to {name}'); return env
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class ProgressCB(BaseCallback):
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def __init__(self, extra): super().__init__(verbose=0); self._last=0; self._extra=extra
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def _on_step(self):
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if self.num_timesteps - self._last >= 10000:
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log(f' +{self.num_timesteps:,} steps (of {self._extra:,} extra)')
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self._last = self.num_timesteps
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return True
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log('='*60)
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log(f'Exp 4: CONTINUE mountain_track from exp3 — adding {EXTRA_STEPS:,} steps')
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log(f' Total training will be 200,000 steps on mountain_track')
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log(f' throttle_min={THROTTLE_MIN}, loading: {os.path.basename(BASE_MODEL)}')
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log('='*60)
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# Switch to mountain_track
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log('Switching to mountain_track...')
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tmp = gym.make('donkey-mountain-track-v0'); time.sleep(2)
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_send_exit_scene(tmp, verbose=False); tmp.close(); time.sleep(5)
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env = VecTransposeImage(DummyVecEnv([lambda: make_env('donkey-mountain-track-v0')]))
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log(f'Loading exp3 model and continuing training...')
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model = PPO.load(BASE_MODEL, env=env, device='cpu')
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model.learn(total_timesteps=EXTRA_STEPS, callback=ProgressCB(EXTRA_STEPS),
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reset_num_timesteps=True)
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model.save(SAVE_PATH)
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log(f'Saved: {SAVE_PATH}.zip')
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env.close(); time.sleep(3)
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def eval_track(current_id, track_id, name, n=3):
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log(f'\n--- EVAL: {name} ---')
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ev = switch_to(current_id, track_id, name)
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m = PPO.load(SAVE_PATH, env=ev, device='cpu')
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for ep in range(1, n+1):
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obs = ev.reset(); total, steps, done = 0.0, 0, False
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while not done and steps < 2000:
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action, _ = m.predict(obs, deterministic=True)
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result = ev.step(action)
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if len(result)==5: obs,r,t,tr,info=result; done=bool(t[0] or tr[0])
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else: obs,r,d,info=result; done=bool(d[0])
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total+=float(r[0]); steps+=1
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status='✅ FULL' if steps>=2000 else f'❌ crash@{steps}'
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log(f' ep{ep}: {total:.0f} reward / {steps} steps — {status}')
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time.sleep(1)
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ev.close(); time.sleep(3)
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return track_id
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log('\nEvaluating deterministic policy on all tracks...')
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current = 'donkey-mountain-track-v0'
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current = eval_track(current, 'donkey-mountain-track-v0', 'mountain_track (training track)')
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current = eval_track(current, 'donkey-generated-track-v0', 'generated_track (zero-shot)')
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current = eval_track(current, 'donkey-minimonaco-track-v0', 'mini_monaco (zero-shot)')
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current = eval_track(current, 'donkey-generated-roads-v0', 'generated_road (zero-shot)')
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log('\n=== Exp 4 COMPLETE ===')
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