The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
<|reference_start|>The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm: 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.<|reference_end|>
@article{park2024the,
    title={The Max-Min Formulation of Multi-Objective Reinforcement Learning: From
  Theory to a Model-Free Algorithm},
    author={Giseung Park, Woohyeon Byeon, Seongmin Kim, Elad Havakuk, Amir Leshem,
  Youngchul Sung},
    journal={arXiv preprint arXiv:2406.07826},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07826},
    primaryClass={cs.LG cs.AI}
}