  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
utilizing 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. The experimental results demonstrate that this approach not only
makes RL agents more anthropomorphic but also reaches better performance.
Additionally, various strategies for reward-proxy and reward-shaping are
investigated, revealing the significant impact of prompt design on shaping an
AD vehicle's behavior. These findings offer a promising direction for the
development of more advanced and human-like automated driving systems. Our
experimental data and source code can be found here.
