Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games

• URL: http://arxiv.org/abs/2202.09422v1
• Date: Fri, 18 Feb 2022 20:35:00 GMT
• Title: Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games
• Authors: Dingyang Chen, Yile Li, Qi Zhang
• Abstract summary: Policy sharing is crucial to efficient learning in certain tasks yet lacks theoretical justification. We develop the first consensus-based decentralized actor-critic method. We also develop practical algorithms based on our decentralized actor-critic method to reduce the communication cost during training.
• Score: 6.589813623221242
• License: http://creativecommons.org/licenses/by/4.0/
• 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 form 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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