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Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems

Qu, Guannan and Wierman, Adam and Li, Na (2019) Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems. . (Unpublished)

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We study reinforcement learning (RL) in a setting with a network of agents whose states and actions interact in a local manner where the objective is to find localized policies such that the (discounted) global reward is maximized. A fundamental challenge in this setting is that the state-action space size scales exponentially in the number of agents, rendering the problem intractable for large networks. In this paper, we propose a Scalable Actor-Critic (SAC) framework that exploits the network structure and finds a localized policy that is a O(ρ^(κ+1))-approximation of a stationary point of the objective for some ρ ∈ (0,1), with complexity that scales with the local state-action space size of the largest κ-hop neighborhood of the network.

Item Type:Report or Paper (Discussion Paper)
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Additional Information:© 2020 G. Qu, A. Wierman & N. Li. To appear in Proceedings of Machine Learning Research.
Subject Keywords:Multi-agent reinforcement learning, networked systems, actor-critic methods
Record Number:CaltechAUTHORS:20200214-105551932
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101299
Deposited By: George Porter
Deposited On:14 Feb 2020 21:10
Last Modified:14 Sep 2022 21:59

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