wave3: fix track-switch bug (viewer not raw socket) + shorten trial budgets
Bug: send_exit_scene_raw() opened a NEW TCP connection, creating a second phantom vehicle. The sim sent exit_scene to the phantom, leaving the real training connection stuck on generated_road for the entire run. Fix: _send_exit_scene() now calls env.unwrapped.viewer.exit_scene() on the EXISTING TCP connection that the training env already holds. This is the only reliable way to switch scenes mid-session (matches track_switcher.py). Also: - Removed send_exit_scene_raw() import from multitrack_runner.py - Simplified initial connection (no spurious exit_scene at startup) - Reduced search space: total_timesteps 80k-400k -> 30k-150k - Reduced seed params: 150k/300k -> 45k/90k (~35-45 min per trial) - Added test: test_close_and_switch_uses_viewer_not_raw_socket 83 tests passing Agent: pi Tests: 83 passed Tests-Added: 1 TypeScript: N/A
This commit is contained in:
Paul Huliganga
parent
4ca5304a71
commit
86657a26b8
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@ -65,7 +65,6 @@ sys.path.insert(0, AGENT_DIR)
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from donkeycar_sb3_runner import ThrottleClampWrapper, SimHealthCallback
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from reward_wrapper import SpeedRewardWrapper
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from track_switcher import send_exit_scene_raw
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# ---- Track catalogue ----
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# Maps short name → gym env ID
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@ -121,33 +120,66 @@ def wrap_env(raw_env):
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# ---- Track switching ----
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def _send_exit_scene(env, verbose=True):
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"""
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Send exit_scene through the EXISTING connection on env.
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Critical: the DonkeyCar sim creates one vehicle per TCP connection.
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Sending exit_scene via a NEW raw socket creates a second vehicle and
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the sim ignores it for the real training session. We must use the
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existing viewer connection that env already holds.
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"""
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try:
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base = env.unwrapped # strips all gym.Wrapper layers
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if hasattr(base, 'viewer') and base.viewer is not None:
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base.viewer.exit_scene() # sends {'msg_type': 'exit_scene'} on existing TCP
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time.sleep(0.5) # let the message flush before closing socket
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if verbose:
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log('[W3 Runner] exit_scene sent on existing viewer connection.')
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return True
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else:
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if verbose:
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log('[W3 Runner] Warning: no viewer found on unwrapped env.')
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return False
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except Exception as e:
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if verbose:
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log(f'[W3 Runner] Warning: viewer.exit_scene() raised: {e}')
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return False
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def close_and_switch(current_env, next_env_id, verbose=True):
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"""
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Cleanly close current env and connect to next track.
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1. env.close() + sleep(2) [ADR-006]
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2. send_exit_scene_raw() + sleep(EXIT_SCENE_WAIT)
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3. gym.make(next_env_id) + wrap
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Correct order (IMPORTANT):
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1. viewer.exit_scene() on existing connection ← tells sim to go to menu
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2. env.close() + sleep(2) ← disconnect (ADR-006)
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3. sleep(EXIT_SCENE_WAIT) ← wait for sim menu
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4. gym.make(next_env_id) + wrap ← connect to new track
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Returns: new wrapped env, or raises on connection failure.
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"""
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if current_env is not None:
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# Step 1: tell the sim to exit the scene BEFORE we close the connection
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if verbose:
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log(f'[W3 Runner] Closing current env before track switch...')
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log('[W3 Runner] Sending exit_scene via existing viewer connection...')
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_send_exit_scene(current_env, verbose=verbose)
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# Step 2: now close the env (ADR-006)
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if verbose:
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log('[W3 Runner] Closing current env...')
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try:
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current_env.close()
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except Exception as e:
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log(f'[W3 Runner] Warning: env.close() raised: {e}')
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time.sleep(2) # ADR-006
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if verbose:
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log(f'[W3 Runner] Sending exit_scene to sim...')
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send_exit_scene_raw()
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# Step 3: wait for sim to reach the scene-selection menu
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if verbose:
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log(f'[W3 Runner] Waiting {EXIT_SCENE_WAIT}s for sim to reach main menu...')
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time.sleep(EXIT_SCENE_WAIT)
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# Step 4: connect to the target track
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if verbose:
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log(f'[W3 Runner] Connecting to {next_env_id}...')
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raw_env = gym.make(next_env_id)
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@ -392,16 +424,13 @@ def main():
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log(f'[W3 Runner] eval_episodes ={args.eval_episodes}')
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# ---- 1. Connect to first training track ----
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# Assume sim is already at the main menu (user-started, or previous run exited cleanly).
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# gym.make() on the first track will load it directly from the menu.
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first_track_name, first_env_id = TRAINING_TRACKS[0]
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log(f'\n[W3 Runner] Starting on first training track: {first_track_name} ({first_env_id})')
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log(f'\n[W3 Runner] Connecting to first training track: {first_track_name} ({first_env_id})')
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env = None
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try:
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# Always send exit_scene first to ensure sim is at main menu
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log(f'[W3 Runner] Sending exit_scene to clean sim state...')
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send_exit_scene_raw()
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time.sleep(EXIT_SCENE_WAIT)
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raw_env = gym.make(first_env_id)
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env = wrap_env(raw_env)
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log(f'[W3 Runner] ✅ Connected to {first_env_id}')
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@ -502,3 +502,42 @@
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[2026-04-14 12:45:34] [Champion] 🏆 NEW BEST! Trial 3: mean_reward=90.0000 params={'r': 90}
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[2026-04-14 12:45:34] [Champion] 🏆 NEW BEST! Trial 5: mean_reward=75.0000 params={'n_steer': 8}
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[2026-04-14 12:45:34] [AutoResearch] Only 1 results — using random proposal.
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[2026-04-14 13:28:43] [AutoResearch] GP UCB top-5 candidates:
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[2026-04-14 13:28:43] UCB=2.3107 mu=0.3981 sigma=0.9563 params={'n_steer': 9, 'n_throttle': 2, 'learning_rate': 0.001405531880392808, 'timesteps': 26173}
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[2026-04-14 13:28:43] UCB=2.3049 mu=0.8602 sigma=0.7224 params={'n_steer': 9, 'n_throttle': 3, 'learning_rate': 0.001793493447174312, 'timesteps': 19198}
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[2026-04-14 13:28:43] UCB=2.2813 mu=0.4904 sigma=0.8954 params={'n_steer': 9, 'n_throttle': 4, 'learning_rate': 0.0011616192816742616, 'timesteps': 13887}
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[2026-04-14 13:28:43] UCB=2.2767 mu=0.5194 sigma=0.8787 params={'n_steer': 9, 'n_throttle': 4, 'learning_rate': 0.0011646447444663046, 'timesteps': 21199}
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[2026-04-14 13:28:43] UCB=2.2525 mu=0.6254 sigma=0.8136 params={'n_steer': 9, 'n_throttle': 3, 'learning_rate': 0.0010196345864901517, 'timesteps': 22035}
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[2026-04-14 13:28:43] [Champion] 🏆 NEW BEST! Trial 1: mean_reward=50.0000 params={'n_steer': 5}
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[2026-04-14 13:28:43] [Champion] 🏆 NEW BEST! Trial 1: mean_reward=80.0000 params={'n_steer': 7}
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[2026-04-14 13:28:43] [Champion] 🏆 NEW BEST! Trial 0: mean_reward=50.0000 params={'r': 50}
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[2026-04-14 13:28:43] [Champion] 🏆 NEW BEST! Trial 1: mean_reward=80.0000 params={'r': 80}
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[2026-04-14 13:28:43] [Champion] 🏆 NEW BEST! Trial 3: mean_reward=90.0000 params={'r': 90}
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[2026-04-14 13:28:43] [Champion] 🏆 NEW BEST! Trial 5: mean_reward=75.0000 params={'n_steer': 8}
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[2026-04-14 13:28:43] [AutoResearch] Only 1 results — using random proposal.
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[2026-04-14 13:29:04] [AutoResearch] GP UCB top-5 candidates:
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[2026-04-14 13:29:04] UCB=2.3107 mu=0.3981 sigma=0.9563 params={'n_steer': 9, 'n_throttle': 2, 'learning_rate': 0.001405531880392808, 'timesteps': 26173}
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[2026-04-14 13:29:04] UCB=2.3049 mu=0.8602 sigma=0.7224 params={'n_steer': 9, 'n_throttle': 3, 'learning_rate': 0.001793493447174312, 'timesteps': 19198}
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[2026-04-14 13:29:04] UCB=2.2813 mu=0.4904 sigma=0.8954 params={'n_steer': 9, 'n_throttle': 4, 'learning_rate': 0.0011616192816742616, 'timesteps': 13887}
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[2026-04-14 13:29:04] UCB=2.2767 mu=0.5194 sigma=0.8787 params={'n_steer': 9, 'n_throttle': 4, 'learning_rate': 0.0011646447444663046, 'timesteps': 21199}
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[2026-04-14 13:29:04] UCB=2.2525 mu=0.6254 sigma=0.8136 params={'n_steer': 9, 'n_throttle': 3, 'learning_rate': 0.0010196345864901517, 'timesteps': 22035}
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[2026-04-14 13:29:04] [Champion] 🏆 NEW BEST! Trial 1: mean_reward=50.0000 params={'n_steer': 5}
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[2026-04-14 13:29:04] [Champion] 🏆 NEW BEST! Trial 1: mean_reward=80.0000 params={'n_steer': 7}
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[2026-04-14 13:29:04] [Champion] 🏆 NEW BEST! Trial 0: mean_reward=50.0000 params={'r': 50}
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[2026-04-14 13:29:04] [Champion] 🏆 NEW BEST! Trial 1: mean_reward=80.0000 params={'r': 80}
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[2026-04-14 13:29:04] [Champion] 🏆 NEW BEST! Trial 3: mean_reward=90.0000 params={'r': 90}
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[2026-04-14 13:29:04] [Champion] 🏆 NEW BEST! Trial 5: mean_reward=75.0000 params={'n_steer': 8}
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[2026-04-14 13:29:04] [AutoResearch] Only 1 results — using random proposal.
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[2026-04-14 13:29:30] [AutoResearch] GP UCB top-5 candidates:
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[2026-04-14 13:29:30] UCB=2.3107 mu=0.3981 sigma=0.9563 params={'n_steer': 9, 'n_throttle': 2, 'learning_rate': 0.001405531880392808, 'timesteps': 26173}
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[2026-04-14 13:29:30] UCB=2.3049 mu=0.8602 sigma=0.7224 params={'n_steer': 9, 'n_throttle': 3, 'learning_rate': 0.001793493447174312, 'timesteps': 19198}
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[2026-04-14 13:29:30] UCB=2.2813 mu=0.4904 sigma=0.8954 params={'n_steer': 9, 'n_throttle': 4, 'learning_rate': 0.0011616192816742616, 'timesteps': 13887}
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[2026-04-14 13:29:30] UCB=2.2767 mu=0.5194 sigma=0.8787 params={'n_steer': 9, 'n_throttle': 4, 'learning_rate': 0.0011646447444663046, 'timesteps': 21199}
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[2026-04-14 13:29:30] UCB=2.2525 mu=0.6254 sigma=0.8136 params={'n_steer': 9, 'n_throttle': 3, 'learning_rate': 0.0010196345864901517, 'timesteps': 22035}
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[2026-04-14 13:29:30] [Champion] 🏆 NEW BEST! Trial 1: mean_reward=50.0000 params={'n_steer': 5}
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[2026-04-14 13:29:30] [Champion] 🏆 NEW BEST! Trial 1: mean_reward=80.0000 params={'n_steer': 7}
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[2026-04-14 13:29:30] [Champion] 🏆 NEW BEST! Trial 0: mean_reward=50.0000 params={'r': 50}
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[2026-04-14 13:29:30] [Champion] 🏆 NEW BEST! Trial 1: mean_reward=80.0000 params={'r': 80}
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[2026-04-14 13:29:30] [Champion] 🏆 NEW BEST! Trial 3: mean_reward=90.0000 params={'r': 90}
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[2026-04-14 13:29:30] [Champion] 🏆 NEW BEST! Trial 5: mean_reward=75.0000 params={'n_steer': 8}
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[2026-04-14 13:29:30] [AutoResearch] Only 1 results — using random proposal.
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@ -18,3 +18,36 @@
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[2026-04-14 12:45:39] [Wave3] Only 0 results — using random proposal.
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[2026-04-14 12:45:39] [Champion] 🏆 NEW BEST! Trial 3: combined=1500.00 (mini_monaco=900.0, warren=600.0) params={'learning_rate': 0.0002, 'steps_per_switch': 8000, 'total_timesteps': 150000}
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[2026-04-14 12:45:39] [Champion] 🏆 NEW BEST! Trial 1: combined=2000.00 (mini_monaco=1200.0, warren=800.0) params={}
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[2026-04-14 12:47:25] =================================================================
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[2026-04-14 12:47:25] [Wave3] Multi-Track Autoresearch — GP+UCB Generalization Search
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[2026-04-14 12:47:25] [Wave3] Training tracks : generated_road, generated_track, mountain_track
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[2026-04-14 12:47:25] [Wave3] Test tracks : mini_monaco, warren (zero-shot)
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[2026-04-14 12:47:25] [Wave3] Max trials : 25 | kappa=2.0 | push every 5
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[2026-04-14 12:47:25] [Wave3] Results file : /home/paulh/projects/donkeycar-rl-autoresearch/agent/outerloop-results/autoresearch_results_phase3.jsonl
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[2026-04-14 12:47:25] [Wave3] Champion dir : /home/paulh/projects/donkeycar-rl-autoresearch/agent/models/wave3-champion
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[2026-04-14 12:47:25] [Wave3] Warm start : /home/paulh/projects/donkeycar-rl-autoresearch/agent/models/champion/model.zip
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[2026-04-14 12:47:25] =================================================================
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[2026-04-14 12:47:25] [Wave3] Loaded 0 existing Phase 3 results.
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[2026-04-14 12:47:25] [Wave3] No Wave 3 champion yet.
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[2026-04-14 12:47:25] [Wave3] Starting from trial 1.
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[2026-04-14 12:47:25]
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[Wave3] ========== Trial 1/25 ==========
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[2026-04-14 12:47:25] [Wave3] Seed trial 1/2: using hardcoded params.
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[2026-04-14 12:47:25] [Wave3] Proposed params: {'learning_rate': 0.000225, 'steps_per_switch': 10000, 'total_timesteps': 150000}
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[2026-04-14 12:47:27] [Wave3] Launching trial 1: {'learning_rate': 0.000225, 'steps_per_switch': 10000, 'total_timesteps': 150000}
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[2026-04-14 12:47:27] [Wave3] Command: python3 /home/paulh/projects/donkeycar-rl-autoresearch/agent/multitrack_runner.py --total-timesteps 150000 --steps-per-switch 10000 --learning-rate 0.000225 --eval-episodes 3 --save-dir /home/paulh/projects/donkeycar-rl-autoresearch/agent/models/wave3-trial-0001 --warm-start /home/paulh/projects/donkeycar-rl-autoresearch/agent/models/champion/model.zip
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[2026-04-14 13:28:47] [Wave3] Seed trial 1/2: using hardcoded params.
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[2026-04-14 13:28:47] [Wave3] Seed trial 2/2: using hardcoded params.
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[2026-04-14 13:28:47] [Wave3] Only 0 results — using random proposal.
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[2026-04-14 13:28:47] [Champion] 🏆 NEW BEST! Trial 3: combined=1500.00 (mini_monaco=900.0, warren=600.0) params={'learning_rate': 0.0002, 'steps_per_switch': 8000, 'total_timesteps': 150000}
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[2026-04-14 13:28:47] [Champion] 🏆 NEW BEST! Trial 1: combined=2000.00 (mini_monaco=1200.0, warren=800.0) params={}
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[2026-04-14 13:29:08] [Wave3] Seed trial 1/2: using hardcoded params.
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[2026-04-14 13:29:08] [Wave3] Seed trial 2/2: using hardcoded params.
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[2026-04-14 13:29:08] [Wave3] Only 0 results — using random proposal.
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[2026-04-14 13:29:08] [Champion] 🏆 NEW BEST! Trial 3: combined=1500.00 (mini_monaco=900.0, warren=600.0) params={'learning_rate': 0.0002, 'steps_per_switch': 8000, 'total_timesteps': 150000}
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[2026-04-14 13:29:08] [Champion] 🏆 NEW BEST! Trial 1: combined=2000.00 (mini_monaco=1200.0, warren=800.0) params={}
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[2026-04-14 13:29:34] [Wave3] Seed trial 1/2: using hardcoded params.
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[2026-04-14 13:29:34] [Wave3] Seed trial 2/2: using hardcoded params.
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[2026-04-14 13:29:34] [Wave3] Only 0 results — using random proposal.
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[2026-04-14 13:29:34] [Champion] 🏆 NEW BEST! Trial 3: combined=1500.00 (mini_monaco=900.0, warren=600.0) params={'learning_rate': 0.0002, 'steps_per_switch': 8000, 'total_timesteps': 150000}
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[2026-04-14 13:29:34] [Champion] 🏆 NEW BEST! Trial 1: combined=2000.00 (mini_monaco=1200.0, warren=800.0) params={}
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@ -63,9 +63,9 @@ os.makedirs(CHAMPION_DIR, exist_ok=True)
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# ---- Hyperparameter search space ----
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PARAM_SPACE = {
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'learning_rate': {'type': 'float', 'min': 5e-5, 'max': 1e-3},
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'steps_per_switch': {'type': 'int', 'min': 2000, 'max': 25000},
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'total_timesteps': {'type': 'int', 'min': 80000, 'max': 400000},
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'learning_rate': {'type': 'float', 'min': 5e-5, 'max': 1e-3},
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'steps_per_switch': {'type': 'int', 'min': 2000, 'max': 15000},
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'total_timesteps': {'type': 'int', 'min': 30000, 'max': 150000},
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}
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PARAM_KEYS = list(PARAM_SPACE.keys())
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@ -81,10 +81,10 @@ JOB_TIMEOUT = 7200 # 2h — 400k steps on CPU may need time
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# ---- Seed trials near Phase 2 champion ----
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# GP warm-up: first 2 trials use known-good parameters so GP has real prior data
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SEED_PARAMS = [
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# Phase 2 champion settings — warm-start, moderate switching
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{'learning_rate': 0.000225, 'steps_per_switch': 10000, 'total_timesteps': 150000},
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# Higher timesteps, less frequent switching — encourage deeper learning per track
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{'learning_rate': 0.000225, 'steps_per_switch': 20000, 'total_timesteps': 300000},
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# 3 full rotations through all 3 training tracks (~35 min per trial)
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{'learning_rate': 0.000225, 'steps_per_switch': 5000, 'total_timesteps': 45000},
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# Slower switching, more time per track (~45 min per trial)
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{'learning_rate': 0.000225, 'steps_per_switch': 10000, 'total_timesteps': 90000},
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]
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@ -188,9 +188,11 @@ def test_close_and_switch_calls_env_close():
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"""close_and_switch() must call env.close() on the old env (ADR-006)."""
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from multitrack_runner import close_and_switch
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old_env = MockGymEnv()
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# Give it a fake viewer so _send_exit_scene has something to call
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mock_viewer = MagicMock()
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old_env.unwrapped.viewer = mock_viewer
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with patch('multitrack_runner.send_exit_scene_raw', return_value=True), \
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patch('multitrack_runner.gym.make', return_value=MockGymEnv()), \
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with patch('multitrack_runner.gym.make', return_value=MockGymEnv()), \
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patch('multitrack_runner.wrap_env', side_effect=lambda e: e), \
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patch('time.sleep'):
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close_and_switch(old_env, 'donkey-generated-track-v0', verbose=False)
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@ -203,8 +205,7 @@ def test_close_and_switch_returns_new_env():
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from multitrack_runner import close_and_switch
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new_env = MockGymEnv()
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with patch('multitrack_runner.send_exit_scene_raw', return_value=True), \
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patch('multitrack_runner.gym.make', return_value=new_env), \
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with patch('multitrack_runner.gym.make', return_value=new_env), \
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patch('multitrack_runner.wrap_env', side_effect=lambda e: e), \
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patch('time.sleep'):
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result = close_and_switch(None, 'donkey-generated-track-v0', verbose=False)
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@ -212,7 +213,23 @@ def test_close_and_switch_returns_new_env():
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assert result is new_env
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||||
def test_evaluate_test_tracks_returns_dict_with_track_keys():
|
||||
def test_close_and_switch_uses_viewer_not_raw_socket():
|
||||
"""exit_scene must be sent via env.unwrapped.viewer, not a new raw connection."""
|
||||
from multitrack_runner import close_and_switch
|
||||
old_env = MockGymEnv()
|
||||
mock_viewer = MagicMock()
|
||||
old_env.unwrapped.viewer = mock_viewer # attach mock viewer
|
||||
|
||||
with patch('multitrack_runner.gym.make', return_value=MockGymEnv()), \
|
||||
patch('multitrack_runner.wrap_env', side_effect=lambda e: e), \
|
||||
patch('time.sleep'):
|
||||
close_and_switch(old_env, 'donkey-generated-track-v0', verbose=False)
|
||||
|
||||
mock_viewer.exit_scene.assert_called_once(), \
|
||||
'exit_scene must be called on existing viewer (not a new raw socket)'
|
||||
|
||||
|
||||
|
||||
"""evaluate_test_tracks() should return a dict keyed by track names."""
|
||||
from multitrack_runner import evaluate_test_tracks, TEST_TRACKS
|
||||
|
||||
|
|
@ -287,8 +304,8 @@ def test_wave3_encode_decode_round_trip():
|
|||
|
||||
original = {
|
||||
'learning_rate': 0.000225,
|
||||
'steps_per_switch': 10000,
|
||||
'total_timesteps': 200000,
|
||||
'steps_per_switch': 5000,
|
||||
'total_timesteps': 90000,
|
||||
}
|
||||
vec = encode_params(original)
|
||||
recovered = decode_params(vec)
|
||||
|
|
@ -504,9 +521,9 @@ def test_wave3_param_space_covers_phase2_champion():
|
|||
# Phase 2 champion: lr=0.000225, which falls in [5e-5, 1e-3]
|
||||
assert PARAM_SPACE['learning_rate']['min'] <= 0.000225 <= PARAM_SPACE['learning_rate']['max']
|
||||
# Moderate switching schedule
|
||||
assert PARAM_SPACE['steps_per_switch']['min'] <= 10000 <= PARAM_SPACE['steps_per_switch']['max']
|
||||
# Phase 2 had 13k timesteps — Phase 3 needs much more; check min >= 50k
|
||||
assert PARAM_SPACE['total_timesteps']['min'] >= 50000
|
||||
assert PARAM_SPACE['steps_per_switch']['min'] <= 5000 <= PARAM_SPACE['steps_per_switch']['max']
|
||||
# Phase 3 needs more than Phase 2's 13k; check min >= 20k
|
||||
assert PARAM_SPACE['total_timesteps']['min'] >= 20000
|
||||
|
||||
|
||||
def test_wave3_seed_params_within_space():
|
||||
|
|
|
|||
Loading…
Reference in New Issue