Deep Reinforcement Learning has made significant progress in multi-agent systems in recent years. In this review article, we have focused on presenting recent approaches on Multi-Agent Reinforcement Learning (MARL) algorithms. In particular, we have focused on five common approaches on modeling and solving cooperative multi-agent reinforcement learning problems: (I) independent learners, (II) fully observable critic, (III) value function factorization, (IV) consensus, and (IV) learn to communicate. First, we elaborate on each of these methods, possible challenges, and how these challenges were mitigated in the relevant papers. If applicable, we further make a connection among different papers in each category. Next, we cover some new emerging research areas in MARL along with the relevant recent papers. Due to the recent success of MARL in real-world applications, we assign a section to provide a review of these applications and corresponding articles. Also, a list of available environments for MARL research is provided in this survey. Finally, the paper is concluded with proposals on the possible research directions.

翻译：近年来,深入强化学习在多试剂系统方面取得了显著进展。在本审查文章中,我们侧重于介绍关于多机构强化学习(MARL)算法的最新方法。特别是,我们侧重于建模和解决合作性多机构强化学习问题的五种共同方法:(一) 独立学习者,(二) 完全可见的批评者,(三) 价值函数因子化,(四) 共识和(四) 学会沟通。首先,我们详细阐述了这些方法中的每一种,可能的挑战,以及如何在相关文件中减轻这些挑战。如果适用的话,我们进一步将每一类不同文件联系起来。接下来,我们把MARL中新出现的一些研究领域与最近的相关文件联系起来。由于MARL在现实世界应用中最近的成功,我们指定了一节来审查这些应用和相应文章。此外,我们在这次调查中提供了MARL研究的现有环境清单。最后,文件最后提出了关于可能的研究方向的建议。