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2024 ICML ICML 2024

The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm

Abstract

No abstract available. No abstract was captured for this paper when its proceedings were scraped - most often an older edition published without one online. How the corpus is built.

Authors

Giseung Park, Woohyeon Byeon, Seongmin Kim, Elad Havakuk, Amir Leshem, Youngchul Sung

Topics

Reinforcement Learning > Methods > Policy Learning

Keywords

fairness multi-objective reinforcement learning performance improvement model-free algorithm theory max-min formulation
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