Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects
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.
Cite
@article{arxiv.2203.10603,
title = {Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects},
author = {Xihuai Wang and Zhicheng Zhang and Weinan Zhang},
journal= {arXiv preprint arXiv:2203.10603},
year = {2024}
}