Qu, Guannan and Wierman, Adam and Li, Na (2019) Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200214-105551932
![]() |
PDF
- Accepted Version
See Usage Policy. 432kB |
Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20200214-105551932
Abstract
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) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Related URLs: |
| |||||||||
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 | |||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20200214-105551932 | |||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
ID Code: | 101299 | |||||||||
Collection: | CaltechAUTHORS | |||||||||
Deposited By: | George Porter | |||||||||
Deposited On: | 14 Feb 2020 21:10 | |||||||||
Last Modified: | 14 Sep 2022 21:59 |
Repository Staff Only: item control page