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donkeycar-rl-autoresearch
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Author SHA1 Message Date
Paul Huliganga 7ed2456896 fix: remove Warren from test set — indoor carpet, broken done condition 
Warren track surface is green carpet (not outdoor road), and the
episode-done condition (|CTE| > max_cte) does not fire when the car
crosses the INSIDE boundary.  Car can drive off-track and bump into
chairs indefinitely, making scores meaningless as a test metric.

Changes:
- multitrack_runner.py: TEST_TRACKS now mini_monaco only
- wave3_controller.py: drop warren_reward from parse/save/champion paths
- tests/test_wave3.py: update assertions to match single test track
- All 83 tests pass

Track classification (final):
  TRAIN : generated_road, generated_track, mountain_track
  TEST  : mini_monaco (outdoor, proper road, correct done condition)
  SKIP  : warren, warehouse, robo_racing_league, waveshare, circuit_launch
  SKIP  : avc_sparkfun (orange markings)

ADR-010 to be updated.

Agent: pi
Tests: 83 passed
Tests-Added: 0
TypeScript: N/A
 2026-04-14 13:47:28 -04:00
Paul Huliganga 86657a26b8 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
 2026-04-14 13:29:49 -04:00
Paul Huliganga 4ca5304a71 wave3: add multi-track autoresearch system (83 tests passing) 
New files:
- agent/multitrack_runner.py: trains PPO round-robin across generated_road,
  generated_track, mountain_track; zero-shot evaluates on mini_monaco + warren
- agent/wave3_controller.py: GP+UCB outer loop optimising combined test score
- tests/test_wave3.py: 30 new tests (83 total)

Track classification (from visual analysis of all 10 screenshots):
  Training  : generated_road, generated_track, mountain_track
  Test (ZSL): mini_monaco, warren (pseudo-outdoor — proper road markings)
  Skip      : warehouse, robo_racing_league, waveshare, circuit_launch (indoor floor)
              avc_sparkfun (orange markings — different visual domain)

Key design decisions:
  ADR-010: Warren = pseudo-outdoor track (proper road lines, not floor marks)
  ADR-011: Test tracks NEVER used in training; GP optimises test score only
  ADR-012: All trials warm-start from Phase 2 champion model
  Switching: env.close() + send_exit_scene_raw() + 4s wait + gym.make()

Pre-Wave-3 baseline: 1/10 tracks drivable (0/2 held-out test tracks)
Wave 3 goal: 2/2 test tracks drivable (mini_monaco + warren)

Agent: pi
Tests: 83 passed
Tests-Added: 30
TypeScript: N/A
 2026-04-14 12:47:12 -04:00