ERFSL: An Efficient Reward Function Searcher via Large Language Models for Custom-Environment Multi-Objective Reinforcement Learning (Student Abstract)
Guanwen Xie, Jingzehua Xu, Yiyuan Yang, Yimian Ding, Shuai Zhang
Abstract
We propose ERFSL, an efficient reward function searcher using large language models (LLMs) for custom-environment, multi-objective reinforcement learning (RL). ERFSL generates reward components based on explicit user requirements and rectifies them, and iteratively optimizes the weights of these components based on textual context. Applied to an underwater data collection RL task, ERFSL corrects reward codes with only one feedback iteration per requirement, and acquires diverse reward functions within the Pareto set. ERFSL also presents robust capability for deviated weights and small-size LLMs such as GPT-4o mini. The full-text prompts, examples of LLM-generated answers, and source code are available at https://360zmem.github.io/LLMRsearcher/ .
BibTeX
@article{Xie_Xu_Yang_Ding_Zhang_2025, title={ERFSL: An Efficient Reward Function Searcher via Large Language Models for Custom-Environment Multi-Objective Reinforcement Learning (Student Abstract)}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35316}, DOI={10.1609/aaai.v39i28.35316}, abstractNote={We propose ERFSL, an efficient reward function searcher using large language models (LLMs) for custom-environment, multi-objective reinforcement learning (RL). ERFSL generates reward components based on explicit user requirements and rectifies them, and iteratively optimizes the weights of these components based on textual context. Applied to an underwater data collection RL task, ERFSL corrects reward codes with only one feedback iteration per requirement, and acquires diverse reward functions within the Pareto set. ERFSL also presents robust capability for deviated weights and small-size LLMs such as GPT-4o mini. The full-text prompts, examples of LLM-generated answers, and source code are available at https://360zmem.github.io/LLMRsearcher/ .}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Xie, Guanwen and Xu, Jingzehua and Yang, Yiyuan and Ding, Yimian and Zhang, Shuai}, year={2025}, month={Apr.}, pages={29535-29537} }