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