A Model-Based Actor-Critic Algorithm in Continuous Time and Space
Identifieur interne : 003860 ( Crin/Corpus ); précédent : 003859; suivant : 003861A Model-Based Actor-Critic Algorithm in Continuous Time and Space
Auteurs : Rémi CoulomSource :
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Abstract
This paper presents a model-based actor-critic algorithm in continuous time and space. Two function approximators are used : one learns the policy (the actor) and the other learns the state-value function (the critic). The critic learns with the TD(lambda) algorithm and the actor by gradient ascent on the Hamiltonian. A similar algorithm had been proposed by Doya, but this one is more general. This algorithm was applied successfully to teach simulated articulated robots to swim.
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" wicri:score="340">A Model-Based Actor-Critic Algorithm in Continuous Time and Space</title></titleStmt><publicationStmt><idno type="RBID">CRIN:coulom03a</idno><date when="2003" year="2003">2003</date><idno type="wicri:Area/Crin/Corpus">003860</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A Model-Based Actor-Critic Algorithm in Continuous Time and Space</title><author><name sortKey="Coulom, Remi" sort="Coulom, Remi" uniqKey="Coulom R" first="Rémi" last="Coulom">Rémi Coulom</name></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>dynamic programming</term><term>optimal control</term><term>reinforcement learning</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en" wicri:score="1727">This paper presents a model-based actor-critic algorithm in continuous time and space. Two function approximators are used : one learns the policy (the actor) and the other learns the state-value function (the critic). The critic learns with the TD(lambda) algorithm and the actor by gradient ascent on the Hamiltonian. A similar algorithm had been proposed by Doya, but this one is more general. This algorithm was applied successfully to teach simulated articulated robots to swim.</div></front></TEI><BibTex type="inproceedings"><ref>coulom03a</ref><crinnumber>A03-R-125</crinnumber><category>3</category><equipe>CORTEX</equipe><author><e>Coulom, Rémi</e></author><title>A Model-Based Actor-Critic Algorithm in Continuous Time and Space</title><booktitle>{Sixth European Workshop on Reinforcement Learning - EWRL6, Nancy, France}</booktitle><year>2003</year><month>Sep</month><url>http://www.loria.fr/publications/2003/A03-R-125/A03-R-125.ps</url><keywords><e>reinforcement learning</e><e>optimal control</e><e>dynamic programming</e></keywords><abstract>This paper presents a model-based actor-critic algorithm in continuous time and space. Two function approximators are used : one learns the policy (the actor) and the other learns the state-value function (the critic). The critic learns with the TD(lambda) algorithm and the actor by gradient ascent on the Hamiltonian. A similar algorithm had been proposed by Doya, but this one is more general. This algorithm was applied successfully to teach simulated articulated robots to swim.</abstract></BibTex></record>Pour manipuler ce document sous Unix (Dilib)
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