In this paper, we consider multi-objective reinforcement learning, which arises in many real-world problems with multiple optimization goals. We approach the problem with a max-min framework focusing on fairness among the multiple goals and develop a relevant theory and a practical model-free algorithm under the max-min framework. The developed theory provides a theoretical advance in multi-objective reinforcement learning, and the proposed algorithm demonstrates a notable performance improvement over existing baseline methods.
@article{arxiv.2406.07826,
title = {The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm},
author = {Giseung Park and Woohyeon Byeon and Seongmin Kim and Elad Havakuk and Amir Leshem and Youngchul Sung},
journal= {arXiv preprint arXiv:2406.07826},
year = {2024}
}