This paper proposes a generative probabilistic model integrating emergent
communication and multi-agent reinforcement learning. The agents plan their
actions by probabilistic inference, called control as inference, and
communicate using messages that are latent variables and estimated based on the
planned actions. Through these messages, each agent can send information about
its actions and know information about the actions of another agent. Therefore,
the agents change their actions according to the estimated messages to achieve
cooperative tasks. This inference of messages can be considered as
communication, and this procedure can be formulated by the Metropolis-Hasting
naming game. Through experiments in the grid world environment, we show that
the proposed PGM can infer meaningful messages to achieve the cooperative task.

本文提出一种生成概率模型，该模型融合了新兴的通信和多智能体强化学习技术，可通过概率推断计划其动作，在通过信息传递实现协作任务的过程中，使用潜在变量估计与规划，基于 Metropolis-Hasting 命名游戏公式推算，实现了有意义的信息传递和协作任务。