Zhang, Yizhou and Qu, Guannan and Xu, Pan and Lin, Yiheng and Chen, Zaiwei and Wierman, Adam (2022) Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20230316-204011712
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Abstract
We study a multi-agent reinforcement learning (MARL) problem where the agents interact over a given network. The goal of the agents is to cooperatively maximize the average of their entropy-regularized long-term rewards. To overcome the curse of dimensionality and to reduce communication, we propose a Localized Policy Iteration (LPI) algorithm that provably learns a near-globally-optimal policy using only local information. In particular, we show that, despite restricting each agent's attention to only its κ-hop neighborhood, the agents are able to learn a policy with an optimality gap that decays polynomially in κ. In addition, we show the finite-sample convergence of LPI to the global optimal policy, which explicitly captures the trade-off between optimality and computational complexity in choosing κ. Numerical simulations demonstrate the effectiveness of LPI.
Item Type: | Report or Paper (Discussion Paper) | ||||||||||||
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Additional Information: | Attribution 4.0 International (CC BY 4.0) | ||||||||||||
Record Number: | CaltechAUTHORS:20230316-204011712 | ||||||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20230316-204011712 | ||||||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||
ID Code: | 120096 | ||||||||||||
Collection: | CaltechAUTHORS | ||||||||||||
Deposited By: | George Porter | ||||||||||||
Deposited On: | 16 Mar 2023 22:55 | ||||||||||||
Last Modified: | 17 Mar 2023 21:31 |
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