A survey of multi-agent deep reinforcement learning with communication

Changxi Zhu*, Mehdi Dastani*, Shihan Wang*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Communication is an effective mechanism for coordinating the behaviors of multiple agents, broadening their views of the environment, and to support their collaborations. In the field of multi-agent deep reinforcement learning (MADRL), agents can improve the overall learning performance and achieve their objectives by communication. Agents can communicate various types of messages, either to all agents or to specific agent groups, or conditioned on specific constraints. With the growing body of research work in MADRL with communication (Comm-MADRL), there is a lack of a systematic and structural approach to distinguish and classify existing Comm-MADRL approaches. In this paper, we survey recent works in the Comm-MADRL field and consider various aspects of communication that can play a role in designing and developing multi-agent reinforcement learning systems. With these aspects in mind, we propose 9 dimensions along which Comm-MADRL approaches can be analyzed, developed, and compared. By projecting existing works into the multi-dimensional space, we discover interesting trends. We also propose some novel directions for designing future Comm-MADRL systems through exploring possible combinations of the dimensions.
Original languageEnglish
Article number4
Number of pages48
JournalAutonomous Agents and Multi-Agent Systems
Volume38
Issue number1
DOIs
Publication statusPublished - 6 Jan 2024

Bibliographical note

Publisher Copyright:
© 2024, The Author(s).

Keywords

  • Communication
  • Deep reinforcement learning
  • Multi-agent reinforcement learning
  • Survey

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