Diffusion Gradient Temporal Difference for Cooperative Reinforcement Learning with Linear Function Approximation

Valcarcel Macua, Sergio, Belanovic, Pavle and Zazo Bello, Santiago ORCID: https://orcid.org/0000-0001-9073-7927 (2012). Diffusion Gradient Temporal Difference for Cooperative Reinforcement Learning with Linear Function Approximation. In: "3rd International Workshop on Cognitive Incromation Processing (CIP)", 28/05/2012 - 30/05/2012, Baiona. ISBN 978-1-4673-1877-8. pp. 1-6.

Description

Title: Diffusion Gradient Temporal Difference for Cooperative Reinforcement Learning with Linear Function Approximation
Author/s:
Item Type: Presentation at Congress or Conference (Article)
Event Title: 3rd International Workshop on Cognitive Incromation Processing (CIP)
Event Dates: 28/05/2012 - 30/05/2012
Event Location: Baiona
Title of Book: 3rd International Workshop on Cognitive Incromation Processing (CIP)
Date: May 2012
ISBN: 978-1-4673-1877-8
Subjects:
Freetext Keywords: TD, distributed reinforcement learning, distributed control, cooperative learning, multi-agent, distributed decision making, distributed temporal difference
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

We introduce a diffusion-based algorithm in which multiple agents cooperate to predict a common and global statevalue function by sharing local estimates and local gradient information among neighbors. Our algorithm is a fully distributed implementation of the gradient temporal difference with linear function approximation, to make it applicable to multiagent settings. Simulations illustrate the benefit of cooperation in learning, as made possible by the proposed algorithm.

More information

Item ID: 20234
DC Identifier: https://oa.upm.es/20234/
OAI Identifier: oai:oa.upm.es:20234
Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...
Deposited by: Memoria Investigacion
Deposited on: 01 Oct 2013 15:52
Last Modified: 21 Apr 2016 22:58
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