635 lines
26 KiB
Python
635 lines
26 KiB
Python
"""
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Wave 3 Multi-Track Runner
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=========================
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Trains PPO across multiple DonkeyCar tracks by round-robin switching between
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training segments. After training, evaluates on zero-shot test tracks
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(mini_monaco + warren) to measure cross-track generalization.
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Track classification (from visual analysis):
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TRAINING : generated_track, mountain_track (Wave 4 — no generated_road, no warm-start)
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(outdoor, same road markings — yellow centre + white edge)
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TEST/EVAL : mini_monaco, warren
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(never seen during training — generalization benchmark)
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SKIPPED : warehouse, robo_racing_league, waveshare, circuit_launch
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(fully indoor — different domain entirely)
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avc_sparkfun (outdoor but orange markings — too different)
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Track switching strategy:
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Close env → send_exit_scene_raw() → wait 4s → gym.make(next_track)
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This avoids the double-connect issue in switch_track() when an env is
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already open on the current track.
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Key invariants (ADR-005, ADR-006):
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- model is always defined before model.save()
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- env.close() + time.sleep(2) before every track switch
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- Results appended to JSONL, never overwritten
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Output lines parsed by wave3_controller.py:
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[W3 Runner][TRAIN] track=<name> segment_reward=<float>
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[W3 Runner][TEST] track=<name> mean_reward=<float> mean_steps=<float>
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[W3 Runner][TEST] combined_test_score=<float>
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Usage:
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python3 multitrack_runner.py \\
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--total-timesteps 200000 \\
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--steps-per-switch 10000 \\
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--learning-rate 0.000225 \\
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--warm-start models/champion/model.zip \\
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--save-dir models/wave3/trial-0001 \\
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--eval-episodes 3
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Exit codes:
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0 — success, model saved, evaluation complete
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100 — failed to connect to simulator on initial track
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101 — training error
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102 — evaluation error
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"""
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import argparse
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import os
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import sys
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import time
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import json
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import numpy as np
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from collections import deque
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from datetime import datetime
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import gymnasium as gym
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import gym_donkeycar
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from stable_baselines3 import PPO
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from stable_baselines3.common.utils import get_schedule_fn
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from stable_baselines3.common.evaluation import evaluate_policy
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from stable_baselines3.common.callbacks import BaseCallback
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# ---- Project paths ----
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AGENT_DIR = os.path.dirname(os.path.abspath(__file__))
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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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# ---- Track catalogue ----
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# Maps short name → gym env ID
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# Wave 4 training tracks.
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# generated_road removed: it is visually too similar to generated_track
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# and its Phase-2-champion warm-start caused catastrophic forgetting that
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# prevented generalisation. generated_track + mountain_track have
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# meaningfully different backgrounds, forcing the model to learn
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# track-marking features rather than scene-specific shortcuts.
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TRAINING_TRACKS = [
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('generated_track', 'donkey-generated-track-v0'),
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('mountain_track', 'donkey-mountain-track-v0'),
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]
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# Zero-shot generalization test tracks — never seen during training.
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# Warren was removed: its episode-done condition does not fire when the car
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# crosses the INSIDE edge (CTE stays small), so the car can drive among
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# chairs indefinitely and scores are meaningless.
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TEST_TRACKS = [
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('mini_monaco', 'donkey-minimonaco-track-v0'),
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]
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# How many steps to sample before deciding the segment reward (shorter than segment)
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SEGMENT_EVAL_STEPS = 500
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EXIT_SCENE_WAIT = 4.0 # seconds after exit_scene for sim to reach menu
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THROTTLE_MIN = 0.2 # minimum throttle (prevents stationary car)
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SPEED_SCALE = 0.1 # SpeedRewardWrapper coefficient
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# ---- Logging ----
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def log(msg):
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ts = datetime.now().strftime('%H:%M:%S')
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print(f'[{ts}] {msg}', flush=True)
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# ---- Health check callback ----
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class HealthCheckCallback(BaseCallback):
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"""Stops training early if sim is stuck or frozen."""
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def __init__(self, max_stuck_steps=150, min_speed=0.02):
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super().__init__(verbose=0)
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self.health = SimHealthCallback(max_stuck_steps=max_stuck_steps, min_speed=min_speed)
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def _on_step(self):
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infos = self.locals.get('infos', [{}])
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obs = self.locals.get('new_obs', None)
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info = infos[0] if infos else {}
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obs_arr = obs[0] if obs is not None and len(obs) > 0 else None
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healthy = self.health.on_step(obs_arr, None, None, info)
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if not healthy:
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log('[W3 Runner][HEALTH] Sim stuck/frozen — stopping segment early.')
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return False
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return True
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class StuckTerminationWrapper(gym.Wrapper):
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"""
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Terminates the episode when the car hasn't made meaningful positional
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progress over `stuck_steps` consecutive steps.
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Handles two cases the sim misses:
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1. Car pressed slowly against a barrier — Unity's hit detection needs a
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velocity threshold; slow contact leaves hit='none' and episode open.
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2. Car circling off the start/finish line — efficiency→0 gives zero reward
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but the episode never ends, wasting training steps with no signal.
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When stuck is detected: terminated=True so SpeedRewardWrapper returns -1.0.
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"""
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def __init__(self, env, stuck_steps: int = 80, min_displacement: float = 0.5):
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super().__init__(env)
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self.stuck_steps = stuck_steps
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self.min_displacement = min_displacement
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self._pos_buf: deque = deque(maxlen=stuck_steps)
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def reset(self, **kwargs):
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self._pos_buf.clear()
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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, reward, terminated, truncated, info = result
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else:
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obs, reward, done, info = result
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terminated, truncated = done, False
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pos = info.get('pos', None)
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if pos is not None:
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try:
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self._pos_buf.append(np.array(list(pos)[:3], dtype=np.float64))
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except (TypeError, ValueError):
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pass
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# Only check once the buffer is full
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if not terminated and len(self._pos_buf) >= self.stuck_steps:
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displacement = float(np.linalg.norm(
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self._pos_buf[-1] - self._pos_buf[0]
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))
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if displacement < self.min_displacement:
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terminated = True
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info['stuck_termination'] = 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 wrap_env(raw_env):
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"""Apply standard wrappers: throttle clamp + stuck detection + speed reward."""
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env = ThrottleClampWrapper(raw_env, 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=SPEED_SCALE)
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return 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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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('[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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# 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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env = wrap_env(raw_env)
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if verbose:
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log(f'[W3 Runner] ✅ Connected to {next_env_id}')
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return env
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# ---- Model creation / warm-start ----
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def create_or_load_model(env, learning_rate, warm_start_path=None, seed=None):
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"""
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Load model from warm_start_path (PPO.load + set_env) or create fresh PPO.
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Falls back to fresh model if warm-start path missing or space mismatch.
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"""
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if warm_start_path and os.path.exists(warm_start_path):
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log(f'[W3 Runner] Loading warm-start model from {warm_start_path}')
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try:
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model = PPO.load(warm_start_path, env=env, device='auto')
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# Three-part LR override required after PPO.load():
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# 1. model.learning_rate — Python attribute (used to recreate lr_schedule)
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# 2. model.lr_schedule — FloatSchedule used by _update_learning_rate()
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# during every train() call. Without this,
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# _update_learning_rate() reverts the optimizer
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# back to the saved LR on the first gradient step.
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# 3. optimizer param_groups — immediate effect before first train()
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model.learning_rate = learning_rate
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model.lr_schedule = get_schedule_fn(learning_rate)
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for pg in model.policy.optimizer.param_groups:
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pg['lr'] = learning_rate
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log(f'[W3 Runner] ✅ Warm start loaded. LR overridden to {learning_rate:.6f} '
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f'(model + lr_schedule + {len(model.policy.optimizer.param_groups)} optimizer param group(s))')
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return model
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except Exception as e:
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log(f'[W3 Runner] ⚠️ Warm start failed ({e}), training from scratch.')
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log(f'[W3 Runner] Creating fresh PPO model (lr={learning_rate:.6f})')
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model = PPO(
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'CnnPolicy',
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env,
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learning_rate=learning_rate,
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verbose=1, # show rollout stats so training progress is visible in log
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seed=seed,
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)
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return model
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# ---- Training loop ----
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def train_multitrack(model, first_env, total_timesteps, steps_per_switch,
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save_dir=None):
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"""
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Train PPO across training tracks by round-robin switching every steps_per_switch steps.
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Args:
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model: PPO model (already set to first_env)
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first_env: The first wrapped training env (already connected)
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total_timesteps: Total training budget across all tracks
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steps_per_switch: Steps per track segment before switching
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Returns:
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env: The last env used (caller must close it)
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segment_rewards: List of (track_name, reward) for each completed segment
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"""
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env = first_env
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steps_done = 0
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track_idx = 0 # Start on generated_road (first in TRAINING_TRACKS)
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segment_rewards = []
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health_cb = HealthCheckCallback()
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log(f'[W3 Runner] Starting multi-track training:')
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log(f' Total timesteps : {total_timesteps:,}')
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log(f' Steps per switch: {steps_per_switch:,}')
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log(f' Training tracks : {[t[0] for t in TRAINING_TRACKS]}')
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log(f' Rotations : ~{total_timesteps // (steps_per_switch * len(TRAINING_TRACKS))} full cycles')
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while steps_done < total_timesteps:
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track_name, track_env_id = TRAINING_TRACKS[track_idx]
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segment_steps = min(steps_per_switch, total_timesteps - steps_done)
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log(f'\n[W3 Runner] === Segment: {track_name} | '
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f'{steps_done:,}/{total_timesteps:,} steps done | '
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f'segment={segment_steps:,} steps ===')
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# Train segment
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model.learn(
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total_timesteps=segment_steps,
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reset_num_timesteps=False, # Continuous timestep counter across segments
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callback=health_cb,
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)
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steps_done += segment_steps
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# --- Checkpoint after every segment ---
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# If the trial is killed (timeout/crash) the latest model is always
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# on disk so results are never completely lost.
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if save_dir:
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try:
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os.makedirs(save_dir, exist_ok=True)
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model.save(os.path.join(save_dir, 'model'))
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log(f'[W3 Runner] Checkpoint saved at step {steps_done:,}')
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except Exception as e:
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log(f'[W3 Runner] WARNING: checkpoint save failed: {e}')
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# Quick segment reward estimate — one deterministic episode,
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# capped at MAX_EVAL_STEPS to prevent non-terminating episodes
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# (e.g. car driving forever on wide generated_track) inflating the metric.
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MAX_EVAL_STEPS = 3000
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try:
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obs = env.reset()
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ep_reward = 0.0
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for _ in range(MAX_EVAL_STEPS):
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action, _ = model.predict(obs, deterministic=True)
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obs, reward, done, info = env.step(action)
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ep_reward += float(reward[0] if hasattr(reward, '__len__') else reward)
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done_flag = done[0] if hasattr(done, '__len__') else done
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if done_flag:
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break
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seg_reward = ep_reward
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log(f'[W3 Runner][TRAIN] track={track_name} segment_reward={seg_reward:.2f}')
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segment_rewards.append((track_name, float(seg_reward)))
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except Exception as e:
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log(f'[W3 Runner][TRAIN] Segment eval failed: {e}')
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segment_rewards.append((track_name, 0.0))
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if steps_done >= total_timesteps:
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break
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# Switch to next training track
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next_track_idx = (track_idx + 1) % len(TRAINING_TRACKS)
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next_track_name, next_env_id = TRAINING_TRACKS[next_track_idx]
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log(f'[W3 Runner] Switching: {track_name} → {next_track_name}')
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try:
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new_env = close_and_switch(env, next_env_id)
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model.set_env(new_env)
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env = new_env
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track_idx = next_track_idx
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except Exception as e:
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log(f'[W3 Runner] ⚠️ Track switch failed: {e}. Retrying in 5s...')
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time.sleep(5)
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try:
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new_env = close_and_switch(None, next_env_id)
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model.set_env(new_env)
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env = new_env
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track_idx = next_track_idx
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except Exception as e2:
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log(f'[W3 Runner] ❌ Track switch retry failed: {e2}. Continuing on current track.')
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# Stay on current track — don't crash the whole run
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log(f'\n[W3 Runner] Training complete: {steps_done:,} total steps across '
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f'{len(segment_rewards)} segments.')
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return env, segment_rewards
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# ---- Zero-shot evaluation on test tracks ----
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def evaluate_test_tracks(model, current_env, eval_episodes):
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"""
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Evaluate the trained model on each test track (zero-shot generalization).
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Switches to each test track, runs eval_episodes episodes, records
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mean_reward and mean_steps. Closes test envs when done.
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Returns:
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test_results: dict of {track_name: {'mean_reward': float, 'mean_steps': float}}
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combined_test_score: sum of mean_rewards across test tracks
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"""
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log(f'\n[W3 Runner] ===== ZERO-SHOT EVALUATION on TEST tracks =====')
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log(f' Test tracks : {[t[0] for t in TEST_TRACKS]}')
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log(f' Eval episodes : {eval_episodes}')
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test_results = {}
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env = current_env
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for track_name, track_env_id in TEST_TRACKS:
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log(f'\n[W3 Runner] Switching to TEST track: {track_name}')
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try:
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env = close_and_switch(env, track_env_id)
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except Exception as e:
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log(f'[W3 Runner] ❌ Cannot connect to test track {track_name}: {e}')
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test_results[track_name] = {'mean_reward': 0.0, 'mean_steps': 0.0, 'error': str(e)}
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continue
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# Run episodes manually to capture step count
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all_rewards = []
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all_steps = []
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for ep in range(eval_episodes):
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obs, info = env.reset()
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total_reward = 0.0
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steps = 0
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done = False
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pos_samples = [] # sample position every 100 steps to detect shuttling
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while not done and steps < 2000:
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action, _ = model.predict(obs, deterministic=True)
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result = env.step(action)
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if len(result) == 5:
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obs, reward, terminated, truncated, info = result
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done = terminated or truncated
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else:
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obs, reward, done, info = result
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total_reward += reward
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steps += 1
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# Sample position every 100 steps for shuttle-exploit detection
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if steps % 100 == 0:
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raw_info = info[0] if isinstance(info, (list, tuple)) else info
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pos = raw_info.get('pos', None) if isinstance(raw_info, dict) else None
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speed = raw_info.get('speed', 0) if isinstance(raw_info, dict) else 0
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if pos is not None:
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pos_samples.append(np.array(list(pos)[:3], dtype=np.float64))
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# Detect shuttle: check if position oscillates rather than progresses
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shuttle_warning = ''
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if len(pos_samples) >= 3:
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# Compute net progress: total displacement from start to end
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net_dist = float(np.linalg.norm(pos_samples[-1] - pos_samples[0]))
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|
# Compute total path between samples
|
|
total_sampled = sum(
|
|
float(np.linalg.norm(pos_samples[i+1] - pos_samples[i]))
|
|
for i in range(len(pos_samples) - 1)
|
|
)
|
|
macro_eff = net_dist / total_sampled if total_sampled > 0.1 else 1.0
|
|
if macro_eff < 0.3 and steps >= 500:
|
|
shuttle_warning = f' ⚠️ SHUTTLE EXPLOIT? macro_efficiency={macro_eff:.2f}'
|
|
|
|
all_rewards.append(total_reward)
|
|
all_steps.append(steps)
|
|
log(f'[W3 Runner] {track_name} ep{ep+1}: reward={total_reward:.1f} steps={steps}'
|
|
f' ({total_reward/max(steps,1):.2f}/step){shuttle_warning}')
|
|
time.sleep(0.5)
|
|
|
|
mean_reward = float(np.mean(all_rewards))
|
|
mean_steps = float(np.mean(all_steps))
|
|
drove_far = mean_steps > 200
|
|
|
|
test_results[track_name] = {
|
|
'mean_reward': mean_reward,
|
|
'mean_steps': mean_steps,
|
|
'drove_far': drove_far,
|
|
}
|
|
verdict = '✅ DRIVES' if drove_far else '❌ CRASHES'
|
|
log(f'[W3 Runner][TEST] track={track_name} mean_reward={mean_reward:.2f} '
|
|
f'mean_steps={mean_steps:.1f} {verdict}')
|
|
|
|
# Combined score = sum of mean_rewards on test tracks
|
|
combined = sum(r['mean_reward'] for r in test_results.values())
|
|
log(f'\n[W3 Runner][TEST] combined_test_score={combined:.4f}')
|
|
log(f'[W3 Runner][TEST] mini_monaco_reward='
|
|
f'{test_results.get("mini_monaco", {}).get("mean_reward", 0.0):.4f}')
|
|
log(f'[W3 Runner][TEST] warren_reward='
|
|
f'{test_results.get("warren", {}).get("mean_reward", 0.0):.4f}')
|
|
|
|
return test_results, combined, env
|
|
|
|
|
|
# ---- Main ----
|
|
def main():
|
|
parser = argparse.ArgumentParser(description='Wave 3 Multi-Track PPO Trainer.')
|
|
parser.add_argument('--total-timesteps', type=int, default=200000,
|
|
help='Total training timesteps across all tracks (default: 200000)')
|
|
parser.add_argument('--steps-per-switch', type=int, default=10000,
|
|
help='Steps on each track before switching (default: 10000)')
|
|
parser.add_argument('--learning-rate', type=float, default=0.000225,
|
|
help='PPO learning rate (default: 0.000225 = Phase 2 champion)')
|
|
parser.add_argument('--warm-start', type=str, default=None,
|
|
help='Path to .zip model for warm start (default: models/champion/model.zip)')
|
|
parser.add_argument('--save-dir', type=str, default=None,
|
|
help='Directory to save trained model')
|
|
parser.add_argument('--eval-episodes', type=int, default=3,
|
|
help='Episodes per test track for zero-shot evaluation')
|
|
parser.add_argument('--seed', type=int, default=None,
|
|
help='Random seed')
|
|
parser.add_argument('--skip-eval', action='store_true',
|
|
help='Skip zero-shot evaluation (training only)')
|
|
args = parser.parse_args()
|
|
|
|
# Wave 4: never auto-detect a warm start. Training always begins from
|
|
# random weights so the CNN is not biased toward any single track.
|
|
warm_start = args.warm_start # None unless caller explicitly passes one
|
|
|
|
save_dir = args.save_dir or os.path.join(AGENT_DIR, 'models', 'wave4',
|
|
f'trial-{int(time.time())}')
|
|
|
|
log(f'[W3 Runner] === Wave 4 Multi-Track Training (scratch, no warm-start) ===')
|
|
log(f'[W3 Runner] total_timesteps ={args.total_timesteps:,}')
|
|
log(f'[W3 Runner] steps_per_switch={args.steps_per_switch:,}')
|
|
log(f'[W3 Runner] learning_rate ={args.learning_rate:.6f}')
|
|
log(f'[W3 Runner] warm_start ={warm_start}')
|
|
log(f'[W3 Runner] save_dir ={save_dir}')
|
|
log(f'[W3 Runner] eval_episodes ={args.eval_episodes}')
|
|
|
|
# ---- 1. Connect to first training track ----
|
|
# Assume sim is already at the main menu (user-started, or previous run exited cleanly).
|
|
# gym.make() on the first track will load it directly from the menu.
|
|
first_track_name, first_env_id = TRAINING_TRACKS[0]
|
|
log(f'\n[W3 Runner] Connecting to first training track: {first_track_name} ({first_env_id})')
|
|
|
|
env = None
|
|
try:
|
|
raw_env = gym.make(first_env_id)
|
|
env = wrap_env(raw_env)
|
|
log(f'[W3 Runner] ✅ Connected to {first_env_id}')
|
|
except Exception as e:
|
|
log(f'[W3 Runner] ❌ Failed to connect to first training track: {e}')
|
|
sys.exit(100)
|
|
|
|
# ---- 2. Create or load model ----
|
|
model = None
|
|
try:
|
|
model = create_or_load_model(env, args.learning_rate, warm_start, args.seed)
|
|
except Exception as e:
|
|
log(f'[W3 Runner] ❌ Model creation failed: {e}')
|
|
try:
|
|
env.close()
|
|
time.sleep(2)
|
|
except Exception:
|
|
pass
|
|
sys.exit(101)
|
|
|
|
# ---- 3. Multi-track training ----
|
|
try:
|
|
env, segment_rewards = train_multitrack(
|
|
model, env,
|
|
total_timesteps=args.total_timesteps,
|
|
steps_per_switch=args.steps_per_switch,
|
|
save_dir=save_dir,
|
|
)
|
|
except Exception as e:
|
|
log(f'[W3 Runner] ❌ Training failed: {e}')
|
|
try:
|
|
env.close()
|
|
time.sleep(2)
|
|
except Exception:
|
|
pass
|
|
sys.exit(101)
|
|
|
|
# ---- 4. Save model ----
|
|
# ADR-005: model is always defined before model.save()
|
|
try:
|
|
os.makedirs(save_dir, exist_ok=True)
|
|
save_path = os.path.join(save_dir, 'model')
|
|
model.save(save_path)
|
|
log(f'[W3 Runner] ✅ Model saved to {save_path}.zip')
|
|
except Exception as e:
|
|
log(f'[W3 Runner] ⚠️ Model save failed: {e}')
|
|
|
|
# ---- 5. Zero-shot evaluation on test tracks ----
|
|
combined_test_score = 0.0
|
|
test_results = {}
|
|
|
|
if not args.skip_eval:
|
|
try:
|
|
test_results, combined_test_score, env = evaluate_test_tracks(
|
|
model, env, args.eval_episodes
|
|
)
|
|
except Exception as e:
|
|
log(f'[W3 Runner] ❌ Test evaluation failed: {e}')
|
|
# Ensure combined_test_score = 0 is recorded (trial still valid)
|
|
|
|
# ---- 6. Print training summary ----
|
|
log(f'\n[W3 Runner] ===== TRAINING SUMMARY =====')
|
|
if segment_rewards:
|
|
by_track = {}
|
|
for tname, rew in segment_rewards:
|
|
by_track.setdefault(tname, []).append(rew)
|
|
for tname, rewards in by_track.items():
|
|
log(f'[W3 Runner][TRAIN] {tname}: '
|
|
f'mean={np.mean(rewards):.1f} over {len(rewards)} segments')
|
|
|
|
log(f'\n[W3 Runner] ===== TEST SUMMARY (zero-shot generalization) =====')
|
|
for tname, metrics in test_results.items():
|
|
verdict = '✅ DRIVES' if metrics.get('drove_far') else '❌ CRASHES'
|
|
log(f'[W3 Runner][TEST] {tname}: '
|
|
f'reward={metrics.get("mean_reward", 0):.1f} '
|
|
f'steps={metrics.get("mean_steps", 0):.0f} {verdict}')
|
|
log(f'[W3 Runner][TEST] combined_test_score={combined_test_score:.4f}')
|
|
|
|
# ---- 7. Teardown ----
|
|
log(f'[W3 Runner] Closing final env...')
|
|
try:
|
|
env.close()
|
|
log(f'[W3 Runner] env.close() complete.')
|
|
except Exception as e:
|
|
log(f'[W3 Runner] Warning: env.close() raised: {e}')
|
|
time.sleep(2) # ADR-006
|
|
|
|
log(f'[W3 Runner] ✅ Multi-track runner complete. Exiting.')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|