Review of the progress of communication-based multi-agent reinforcement learning

Abstract

Reinforcement learning (RL) technology has been successfully applied to various continuous decision environments in decades of development. Nowadays, RL is attracting more attention, even being touted as one of the closest approaches to general artificial intelligence. However, real-world problems often involve multiple intelligent agents interacting with each other. Thus, we focus on multi-agent reinforcement learning (MARL) to deal with such multi-agent systems in practice. In the past decade, the combination of multi-agent system and RL has become increasingly close, gradually forming and enriching the research field of MARL. Reviewing the studies on MARL, we found that researchers mainly solve MARL problems from three perspectives: learning framework, joint action learning, and communication-based MARL. In this paper, we focus from the studies on the communication perspective. We first state the reasons for choosing communication-based MARL and then list the president studies falling into the MARL category but different in nature. We hope that this article can provide a reference for developing MARL methods that can solve practical problems for the national welfare.