Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects
It provides a comprehensive overview for researchers in AI and machine learning, but it is incremental as it synthesizes existing work without introducing new methods or results.
This paper reviews model-based multi-agent reinforcement learning (MARL) to address the high sample inefficiency in MARL by leveraging model-based methods for improved sample efficiency, analyzing existing algorithms, their advantages, and future directions.
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.