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Bringing Autonomous Driving  RL to OpenEnv and TRL
reacted to sergiopaniego's post with 🚀 3 days ago
What happens when you make an LLM drive a car where physics are real and actions can't be undone? I ported CARLA, the autonomous driving simulator, to OpenEnv and added training support via TRL + Hugging Face Spaces. The model interacts with the simulator through tool calls (observe, brake, change lane) and learns from a reward signal. In 50 training steps, Qwen 0.6B learns to swerve and brake to avoid pedestrians in emergency situations. The project supports text and vision (VLMs can see through a camera sensor), open-world driving with traffic, and multiple driving scenarios. This builds on the carla-env project by sinatras, which originally placed LLMs inside CARLA for evaluation. We extended it with vision, new scenarios, rubric-based rewards, and made it trainable end-to-end. Blog: https://huggingface.co/blog/sergiopaniego/bringing-carla-to-openenv-trl/ CARLA env in OpenEnv: https://github.com/meta-pytorch/OpenEnv/tree/main/envs/carla_env Training script: https://github.com/huggingface/trl/blob/main/examples/scripts/openenv/carla.py
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