Hong-Ye Zhu

$R^2E:$ A Role-driven Reward Evolutionary Framework for Automated Reward Function Design

Designing effective reward functions is a critical challenge in Reinforcement Learning (RL), which traditionally requires costly manual trial-and-error. While Large Language Model (LLM)-based methods have shown promise in reward design, undifferentiated sampling and limited use of historical feedback often lead to insu...