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The multi-agent system (MAS) enables the sharing of capabilities amongagents, such that collaborative tasks can be accomplished with high scalabilityand efficiency. MAS is increasingly widely applied in various fields.Meanwhile, the large-scale and time-sensitive data transmission between agentsbrings challenges to the communication system. The traditional wirelesscommunication ignores the content of the data and its impact on the taskexecution at the receiver, which makes it difficult to guarantee the timelinessand relevance of the information. This limitation leads to that traditionalwireless communication struggles to effectively support emerging multi-agentcollaborative applications. Faced with this dilemma, task-orientedcommunication is a potential solution, which aims to transmit task-relevantinformation to improve task execution performance. However, multi-agentcollaboration itself is a complex class of sequential decision problems. It ischallenging to explore efficient information flow in this context. In thisarticle, we use deep reinforcement learning (DRL) to explore task-orientedcommunication in MAS. We begin with a discussion on the application of DRL totask-oriented communication. We then envision a task-oriented communicationarchitecture for MAS, and discuss the designs based on DRL. Finally, we discussopen problems for future research and conclude this article.

Exploring Task-oriented Communication in Multi-agent System: A Deep Reinforcement Learning Approach