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Pedro Cuenca
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Bringing Autonomous Driving RL to OpenEnv and TRL
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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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pcuenq/coco-2017-mirror
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pcuenq/coco2017-instances
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pcuenq/MMLU-Pro-json
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pcuenq/misc-assets
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pcuenq/media
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pcuenq/gists
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pcuenq/recipe-images
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pcuenq/face_synthetics_spiga
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pcuenq/face_synthetics_spiga_smol
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pcuenq/face_synthetics_smol
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pcuenq/lsun-bedrooms
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pcuenq/repro
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pcuenq/CelebA-faces-cropped-128-encoded
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pcuenq/stable-diffusion-flagging
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