论文标题
基于型号的多代理强化学习：最新进度和前景
Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects
论文作者
论文摘要
最近在多代理增强学习（MARL）中取得了重大进步，该学习解决了涉及多个参与者的顺序决策问题。但是，MAL需要大量的样本来进行有效的培训。另一方面，已经证明基于模型的方法可实现样本效率的可证明优势。但是，基于模型的MAL方法的尝试刚刚开始。本文介绍了现有的基于模型MARL的研究，包括理论分析，算法和应用程序，并分析了基于模型的MARL的优势和潜力。具体而言，我们根据多代理方案固有的挑战提供了算法的详细分类法，并指出每种算法的利弊。我们还概述了该领域未来发展的有希望的方向。
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.
