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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2406.07826 |
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| _version_ | 1866914832386621440 |
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| author | Park, Giseung Byeon, Woohyeon Kim, Seongmin Havakuk, Elad Leshem, Amir Sung, Youngchul |
| author_facet | Park, Giseung Byeon, Woohyeon Kim, Seongmin Havakuk, Elad Leshem, Amir Sung, Youngchul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07826 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm Park, Giseung Byeon, Woohyeon Kim, Seongmin Havakuk, Elad Leshem, Amir Sung, Youngchul Machine Learning Artificial Intelligence 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. |
| title | The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.07826 |