KEY FIX: env.unwrapped.viewer.exit_scene() sends exit_scene through the proper established websocket connection. The previous raw socket approach failed because DonkeyCar uses a specific TCP protocol framing. Working flow: 1. Connect to current scene using gym.make(current_env_id) 2. env.unwrapped.viewer.exit_scene() — sends exit via websocket 3. Wait 4s for sim to return to main menu 4. gym.make(target_env_id) — sim now loads the correct scene (loading scene X confirmed) This enables fully automated multi-track evaluation and training without user intervention. Confirmed working: generated_track → generated_road switch verified. Agent: pi/claude-sonnet Tests: 53/53 passing Tests-Added: 0 TypeScript: N/A |
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| .harness | ||
| agent | ||
| docs | ||
| tests | ||
| .gitignore | ||
| AGENT.md | ||
| DECISIONS.md | ||
| IMPLEMENTATION_PLAN.md | ||
| PROJECT-KICKOFF.md | ||
| PROJECT-SPEC.md | ||
| README.md | ||
| create_gitea_repo.py | ||
| ralph-loop.sh | ||
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