A Dantzig Selector Approach to Temporal Difference Learning
Identifieur interne : 001A58 ( Main/Exploration ); précédent : 001A57; suivant : 001A59A Dantzig Selector Approach to Temporal Difference Learning
Auteurs : Matthieu Geist [France] ; Bruno Scherrer [France] ; Alessandro Lazaric [France] ; Mohammad Ghavamzadeh [France]Source :
Abstract
LSTD is one of the most popular reinforcement learning algorithms for value function approximation. Whenever the number of samples is larger than the number of features, LSTD must be paired with some form of regularization. In particular, L1-regularization methods tends to perform feature selection by promoting sparsity and thus they are particularly suited in high-dimensional problems. Nonetheless, since LSTD is not a simple regression algorithm but it solves a fixed-point problem, the integration with L1-regularization is not straightforward and it might come with some drawbacks (see e.g., the P-matrix assumption for LASSO-TD). In this paper we introduce a novel algorithm obtained by integrating LSTD with the Dantzig Selector. In particular, we investigate the performance of the algorithm and its relationship with existing regularized approaches, showing how it overcomes some of the drawbacks of existing solutions.
Url:
Affiliations:
- France
- Franche-Comté, Grand Est, Lorraine (région)
- Besançon, Metz, Nancy
- Université Paul Verlaine - Metz, Université de Bourgogne Franche-Comté, Université de Franche-Comté, Université de Lorraine
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CS 20048 59651 Villeneuve d'Ascq Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.ec-lille.fr/</ref></desc></org></tutelle><tutelle active="#struct-104752" type="direct"><org type="laboratory" xml:id="struct-104752" status="VALID"><idno type="RNSR">200818245B</idno><orgName>INRIA Lille - Nord Europe</orgName><desc><address><addrLine>Parc Scientifique de la Haute Borne 40, avenue Halley Bât.A, Park Plaza 59650 Villeneuve d'Ascq</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/lille/</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">LSTD is one of the most popular reinforcement learning algorithms for value function approximation. Whenever the number of samples is larger than the number of features, LSTD must be paired with some form of regularization. In particular, L1-regularization methods tends to perform feature selection by promoting sparsity and thus they are particularly suited in high-dimensional problems. Nonetheless, since LSTD is not a simple regression algorithm but it solves a fixed-point problem, the integration with L1-regularization is not straightforward and it might come with some drawbacks (see e.g., the P-matrix assumption for LASSO-TD). In this paper we introduce a novel algorithm obtained by integrating LSTD with the Dantzig Selector. In particular, we investigate the performance of the algorithm and its relationship with existing regularized approaches, showing how it overcomes some of the drawbacks of existing solutions.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Franche-Comté</li><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Besançon</li><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université Paul Verlaine - Metz</li><li>Université de Bourgogne Franche-Comté</li><li>Université de Franche-Comté</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Franche-Comté"><name sortKey="Geist, Matthieu" sort="Geist, Matthieu" uniqKey="Geist M" first="Matthieu" last="Geist">Matthieu Geist</name></region><name sortKey="Ghavamzadeh, Mohammad" sort="Ghavamzadeh, Mohammad" uniqKey="Ghavamzadeh M" first="Mohammad" last="Ghavamzadeh">Mohammad Ghavamzadeh</name><name sortKey="Lazaric, Alessandro" sort="Lazaric, Alessandro" uniqKey="Lazaric A" first="Alessandro" last="Lazaric">Alessandro Lazaric</name><name sortKey="Scherrer, Bruno" sort="Scherrer, Bruno" uniqKey="Scherrer B" first="Bruno" last="Scherrer">Bruno Scherrer</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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