Author

Shigeng Zhang

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Preprint Oct 2026

$R^2E:$ 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...

Shou-Hao Chang, Xuan Liu, Hong-Ye Zhu et al. · 0 citations

Jul 2026

Learning from success: efficient selective learning methods for multi-agent sparse-reward tasks

This paper proposes effective multi-agent selective learning methods to boost sample-efficient training by learning from successful experiences, and adopts a retrogression-based selection method to identify successful agent trajectories from the team rewards.

Xin-Ning Chen, Xuan Liu, Yanwen Ba et al. · CCF Transactions on Pervasiv... · 0 citations