Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2111.12961v1
• Date: Thu, 25 Nov 2021 08:07:30 GMT
• Title: Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning
• Authors: Xiaoxiao Zhao, Jinlong Lei, Li Li
• Abstract summary: This paper studies a distributed policy gradient in collaborative multi-agent reinforcement learning (MARL) Agents over a communication network aim to find the optimal policy to maximize the average of all agents' local returns.
• Score: 7.4447396913959185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper studies a distributed policy gradient in collaborative multi-agent reinforcement learning (MARL), where agents over a communication network aim to find the optimal policy to maximize the average of all agents' local returns. Due to the non-concave performance function of policy gradient, the existing distributed stochastic optimization methods for convex problems cannot be directly used for policy gradient in MARL. This paper proposes a distributed policy gradient with variance reduction and gradient tracking to address the high variances of policy gradient, and utilizes importance weight to solve the non-stationary problem in the sampling process. We then provide an upper bound on the mean-squared stationary gap, which depends on the number of iterations, the mini-batch size, the epoch size, the problem parameters, and the network topology. We further establish the sample and communication complexity to obtain an $\epsilon$-approximate stationary point. Numerical experiments on the control problem in MARL are performed to validate the effectiveness of the proposed algorithm.

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