Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games
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.
Preprint version The Tenth International Conference on Learning Representations (ICLR 2022), 2022.
© The Authors, 2022
Chen, D., Li, Y., & Zhang, Q. (2022). Communication-efficient actor-critic methods for homogeneous Markov games. International Conference on Learning Representations. https://openreview.net/forum?id=xy_2w3J3kH