About communication in Multi-Agent Reinforcement Learning

多智能体强化学习(MARL)中的通信是一个关键组件,对最终的智能体性能有显著影响,尤其在合作、协调和谈判中。本文回顾了不同研究如何使用深度神经网络学习通信协议,包括《通过深度多智能体强化学习学习通信》、《多智能体深度强化学习中的社会影响力作为内在动机》和《多智能体通信的目标方法》三篇论文,探讨了通信协议的中央/分散学习与执行的权衡,并展示了在复杂环境中有效通信的好处。

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Communication is one of the components of MARL and an active area of research itself, as it might influence the final performance of agents, and it affects coordination or negotiation directly. Effective communication is essential in order to interact successfully, solving the challenges of cooperation, coordination, and negotiation between several agents.

Most research in multiagent systems has tried to address the communication needs of an agent: what information to send, when, and to whom and result in strategies that are optimized for the specific application for which they are adopted. Known communication protocols such as Cheap talk can be seen as “doing by talking” in which talk precedes action. Others include “talking by doing” in which

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