Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving

• URL: http://arxiv.org/abs/2410.12568v2
• Date: Sun, 20 Oct 2024 04:35:34 GMT
• Title: Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving
• Authors: Sihao Wu, Jiaxu Liu, Xiangyu Yin, Guangliang Cheng, Xingyu Zhao, Meng Fang, Xinping Yi, Xiaowei Huang,
• Abstract summary: This paper introduces RAPID, a novel framework for training mix-of-policy Reinforcement Learning agents. It trains specialized mix-of-policy RL agents using data synthesized by an LLM-based driving agent and online adaptation. It reduces the robustness of LLM knowledge while maintaining adaptability to different tasks.
• Score: 41.87011820577736
• License:
• Abstract: The integration of Large Language Models (LLMs) into autonomous driving systems demonstrates strong common sense and reasoning abilities, effectively addressing the pitfalls of purely data-driven methods. Current LLM-based agents require lengthy inference times and face challenges in interacting with real-time autonomous driving environments. A key open question is whether we can effectively leverage the knowledge from LLMs to train an efficient and robust Reinforcement Learning (RL) agent. This paper introduces RAPID, a novel \underline{\textbf{R}}obust \underline{\textbf{A}}daptive \underline{\textbf{P}}olicy \underline{\textbf{I}}nfusion and \underline{\textbf{D}}istillation framework, which trains specialized mix-of-policy RL agents using data synthesized by an LLM-based driving agent and online adaptation. RAPID features three key designs: 1) utilization of offline data collected from an LLM agent to distil expert knowledge into RL policies for faster real-time inference; 2) introduction of robust distillation in RL to inherit both performance and robustness from LLM-based teacher; and 3) employment of a mix-of-policy approach for joint decision decoding with a policy adapter. Through fine-tuning via online environment interaction, RAPID reduces the forgetting of LLM knowledge while maintaining adaptability to different tasks. Extensive experiments demonstrate RAPID's capability to effectively integrate LLM knowledge into scaled-down RL policies in an efficient, adaptable, and robust way. Code and checkpoints will be made publicly available upon acceptance.

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