Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects

• URL: http://arxiv.org/abs/2203.10603v1
• Date: Sun, 20 Mar 2022 17:24:47 GMT
• Title: Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects
• Authors: Xihuai Wang, Zhicheng Zhang, Weinan Zhang
• Abstract summary: Multi-Agent Reinforcement Learning (MARL) tackles sequential decision-making problems involving multiple participants. MARL requires a tremendous number of samples for effective training. Model-based methods have been shown to achieve provable advantages of sample efficiency.
• Score: 23.347535672670688
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Significant advances have recently been achieved in Multi-Agent Reinforcement Learning (MARL) which tackles sequential decision-making problems involving multiple participants. However, MARL requires a tremendous number of samples for effective training. On the other hand, model-based methods have been shown to achieve provable advantages of sample efficiency. However, the attempts of model-based methods to MARL have just started very recently. This paper presents a review of the existing research on model-based MARL, including theoretical analyses, algorithms, and applications, and analyzes the advantages and potential of model-based MARL. Specifically, we provide a detailed taxonomy of the algorithms and point out the pros and cons for each algorithm according to the challenges inherent to multi-agent scenarios. We also outline promising directions for future development of this field.

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