Human-centric Reward Optimization for Reinforcement Learning-based Automated Driving using Large Language Models

Understand

One of the key challenges in current Reinforcement Learning (RL)-based Automated Driving (AD) agents is achieving flexible, precise, and human-like behavior cost-effectively.

• This paper introduces an innovative approach that uses large language models (LLMs) to intuitively and effectively optimize RL reward functions in a human-centric way.
• We developed a framework where instructions and dynamic environment descriptions are input into the LLM.
• The LLM then utilizes this information to assist in generating rewards, thereby steering the behavior of RL agents towards patterns that more closely resemble human driving.