Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning
This work addresses scalability and efficiency issues in collaborative multi-agent systems, representing an incremental improvement over existing methods.
The paper tackles the problem of high variance and distribution shift in distributed policy gradient methods for multi-agent reinforcement learning, proposing a variance reduction and gradient tracking approach that achieves sample and communication complexity bounds for finding an ε-approximate stationary point.
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 {distribution shift} 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 $ε$-approximate stationary point. Numerical experiments are performed to validate the effectiveness of the proposed algorithm.