Communication-Efficient Actor-Critic Methods for Homogeneous Markov GamesDownload PDF

12 Oct 2021, 19:37 (modified: 01 Dec 2021, 02:56)Deep RL Workshop NeurIPS 2021Readers: Everyone
Keywords: multi-agent
Abstract: Recent success in cooperative multi-agent reinforcement learning (MARL) relies on centralized training and policy sharing. Centralized training eliminates the issue of non-stationarity MARL yet induces large communication costs, and policy sharing is empirically crucial to efficient learning in certain tasks yet lacks theoretical justification. In this paper, we formally characterize a subclass of cooperative Markov games where agents exhibit a certain level of homogeneity such that policy sharing provably incurs no suboptimality. This enables us to develop the first consensus-based decentralized actor-critic method where the consensus update is applied to both the actors and the critics while ensuring convergence. We also develop practical algorithms based on our decentralized actor-critic method to reduce the communication cost during training, while still yielding policies comparable with centralized training.
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