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arXiv · 2006.06555

Multi-Agent Reinforcement Learning in Stochastic Networked Systems

Yiheng Lin·Guannan Qu·Longbo Huang·Adam Wierman
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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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BibTeXRIS

Yiheng Lin, Guannan Qu, Longbo Huang, Adam Wierman. 2020-06-11. Multi-Agent Reinforcement Learning in Stochastic Networked Systems. https://arxiv.org/abs/2006.06555

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