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arXiv · 2004.08883

Variational Policy Propagation for Multi-agent Reinforcement Learning

Chao Qu·Hui Li·Chang Liu·Junwu Xiong·James Zhang·Wei Chu·Weiqiang Wang·Yuan Qi·Le Song
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Abstract

We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions over agents. We prove that the joint policy is a Markov Random Field under some mild conditions, which in turn reduces the policy space effectively. We integrate the variational inference as special differentiable layers in policy such that the actions can be efficiently sampled from the Markov Random Field and the overall policy is differentiable. We evaluate our algorithm on several large scale challenging tasks and demonstrate that it outperforms previous state-of-the-arts.

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Chao Qu, Hui Li, Chang Liu, Junwu Xiong, James Zhang, Wei Chu, Weiqiang Wang, Yuan Qi, Le Song. 2020-04-19. Variational Policy Propagation for Multi-agent Reinforcement Learning. https://arxiv.org/abs/2004.08883

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