A survey of multi-agent deep reinforcement learning with communication

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...

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Bibliographic Details
Published in:Autonomous agents and multi-agent systems Vol. 38; no. 1; p. 4
Main Authors: Zhu, Changxi, Dastani, Mehdi, Wang, Shihan
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2024
Springer Nature B.V
Subjects:
Artificial Intelligence
Communication
Computer Science
Computer Systems Organization and Communication Networks
Deep learning
Language
Multiagent systems
Software Engineering/Programming and Operating Systems
User Interfaces and Human Computer Interaction
Multi-agent reinforcement learning
Deep reinforcement learning
Survey
Communication
ISSN:1387-2532, 1573-7454
Online Access:Get full text
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Summary: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.
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ISSN:1387-2532
1573-7454
DOI:10.1007/s10458-023-09633-6