Multi-Agent Reinforcement Learning in Stochastic Networked Systems

• URL: http://arxiv.org/abs/2006.06555v3
• Date: Tue, 2 Nov 2021 00:34:46 GMT
• Title: Multi-Agent Reinforcement Learning in Stochastic Networked Systems
• Authors: Yiheng Lin, Guannan Qu, Longbo Huang, Adam Wierman
• Abstract summary: We study multi-agent reinforcement learning (MARL) in a network of agents. The objective is to find localized policies that maximize the (discounted) global reward.
• Score: 30.78949372661673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study multi-agent reinforcement learning (MARL) in a stochastic network of agents. The objective is to find localized policies that maximize the (discounted) global reward. In general, scalability is a challenge in this setting because the size of the global state/action space can be exponential in the number of agents. Scalable algorithms are only known in cases where dependencies are static, fixed and local, e.g., between neighbors in a fixed, time-invariant underlying graph. In this work, we propose a Scalable Actor Critic framework that applies in settings where the dependencies can be non-local and stochastic, and provide a finite-time error bound that shows how the convergence rate depends on the speed of information spread in the network. Additionally, as a byproduct of our analysis, we obtain novel finite-time convergence results for a general stochastic approximation scheme and for temporal difference learning with state aggregation, which apply beyond the setting of MARL in networked systems.

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