ICML 2024poster2 citations
The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
Giseung Park, Woohyeon Byeon, Seongmin Kim, Elad Havakuk, Amir Leshem, Youngchul Sung
Abstract
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.
BibTeX
@inproceedings{
park2024the,
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},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=cY9g0bwiZx}
}