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donkeycar-rl-autoresearch
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Research-grade DonkeyCar RL autoresearch and sweep system.
10 Commits 1 Branch 0 Tags 207 MiB
Python 94.3%
Shell 5.7%
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Paul Huliganga 5e93dae316 fix: hack-proof reward shaping + reward hacking detection + research log 
CRITICAL BUG FIX — Reward Hacking:
- Old formula: speed × (1 - cte/max_cte) could be maximized by oscillating
  at track boundary regardless of on-track behavior (trials 8+13 hit 1936+1139)
- New formula: original_reward × (1 + speed_scale × speed) ONLY when on_track
- Off-track (original_reward ≤ 0) → zero speed bonus → cannot be hacked
- Verified hack-proof: 9 new targeted tests including test_cannot_hack_by_going_fast_off_track

Reward Hacking Auto-Detection:
- check_for_reward_hacking() flags results with >3.0 reward/step as suspected hacking
- Flagged results are excluded from GP fitting (won't optimize toward hacking params)
- reward_hacking_suspected field added to JSONL result records

Research Documentation:
- docs/RESEARCH_LOG.md created: full chronological research history
  - Random policy bug discovery and impact
  - Throttle clamp fix
  - Reward hacking discovery with evidence table
  - Hack-proof design rationale
  - Lessons learned + future research questions
- Archived corrupted Phase 1 data: autoresearch_results_phase1_CORRUPTED_reward_hacking.jsonl
- Archived hacked models: models/ARCHIVED_reward_hacking/

Clean start: autoresearch_results_phase1.jsonl reset, models/champion reset

Agent: pi/claude-sonnet
Tests: 40/40 passing
Tests-Added: +9 (reward wrapper hack-proof tests)
TypeScript: N/A
 		2026-04-13 12:27:48 -04:00
.harness feat: Wave 1 complete — real PPO training, model save, GP+UCB autoresearch, 37 tests passing 2026-04-13 10:03:15 -04:00
agent fix: hack-proof reward shaping + reward hacking detection + research log 2026-04-13 12:27:48 -04:00
docs fix: hack-proof reward shaping + reward hacking detection + research log 2026-04-13 12:27:48 -04:00
tests fix: hack-proof reward shaping + reward hacking detection + research log 2026-04-13 12:27:48 -04:00
.gitignore feat: Wave 1 complete — real PPO training, model save, GP+UCB autoresearch, 37 tests passing 2026-04-13 10:03:15 -04:00
AGENT.md feat: Wave 1 complete — real PPO training, model save, GP+UCB autoresearch, 37 tests passing 2026-04-13 10:03:15 -04:00
DECISIONS.md feat: Wave 1 complete — real PPO training, model save, GP+UCB autoresearch, 37 tests passing 2026-04-13 10:03:15 -04:00
IMPLEMENTATION_PLAN.md feat: Wave 1 complete — real PPO training, model save, GP+UCB autoresearch, 37 tests passing 2026-04-13 10:03:15 -04:00
PROJECT-KICKOFF.md feat: Wave 1 complete — real PPO training, model save, GP+UCB autoresearch, 37 tests passing 2026-04-13 10:03:15 -04:00
PROJECT-SPEC.md feat: Wave 1 complete — real PPO training, model save, GP+UCB autoresearch, 37 tests passing 2026-04-13 10:03:15 -04:00
README.md feat: Wave 1 complete — real PPO training, model save, GP+UCB autoresearch, 37 tests passing 2026-04-13 10:03:15 -04:00
create_gitea_repo.py Initial commit 2026-04-12 23:44:36 -04:00
ralph-loop.sh feat: Wave 1 complete — real PPO training, model save, GP+UCB autoresearch, 37 tests passing 2026-04-13 10:03:15 -04:00

README.md

donkeycar-rl-autoresearch

Purpose

Status

  • Scaffolded with the agent harness
  • Spec not filled yet

Runbook

  • Fill PROJECT-SPEC.md
  • Create IMPLEMENTATION_PLAN.md from the spec
  • Start the implementation loop