A review of cooperative multi-agent deep reinforcement learning

Abstract

Deep Reinforcement Learning has made significant progress in multi-agent systems in recent years. The aim of this review article is to provide an overview of recent approaches on Multi-Agent Reinforcement Learning (MARL) algorithms. Our classification of MARL approaches includes five categories for modeling and solving cooperative multi-agent reinforcement learning problems: (I) independent learners, (II) fully observable critics, (III) value function factorization, (IV) consensus, and (IV) learn to communicate. We first discuss each of these methods, their potential challenges, and how these challenges were mitigated in the relevant papers. Additionally, we make connections among different papers in each category if applicable. Next, we cover some new emerging research areas in MARL along with the relevant recent papers. In light of MARL’s recent success in real-world applications, we have dedicated a section to reviewing these applications and articles. This survey also provides a list of available environments for MARL research. Finally, the paper is concluded with proposals on possible research directions.