Policy search for path integral control

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Gómez, Vicençca
• dc.contributor.author Kappen, Hilbert J.ca
• dc.contributor.author Peters, Jan-Michaelca
• dc.contributor.author Neumann, Gerhardca
• dc.date.accessioned 2017-07-04T08:57:32Z
• dc.date.available 2017-07-04T08:57:32Z
• dc.date.issued 2014
• dc.description Comunicació presentada a la European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2014), celebrada els dies 15 a 19 de setembre de 2014 a Nancy, França.
• dc.description.abstract Path integral (PI) control defines a general class of control problems for which the optimal control computation is equivalent to an inference problem that can be solved by evaluation of a path integral over state trajectories. However, this potential is mostly unused in real-world problems because of two main limitations: first, current approaches can typically only be applied to learn open-loop controllers and second, current sampling procedures are inefficient and not scalable to high dimensional systems. We introduce the efficient Path Integral Relative-Entropy Policy Search (PI-REPS) algorithm for learning feedback policies with PI control. Our algorithm is inspired by information theoretic policy updates that are often used in policy search. We use these updates to approximate the state trajectory distribution that is known to be optimal from the PI control theory. Our approach allows for a principled treatment of different sampling distributions and can be used to estimate many types of parametric or non-parametric feedback controllers. We show that PI-REPS significantly outperforms current methods and is able to solve tasks that are out of reach for current methods.
• dc.description.sponsorship This work was supported by the European Community Seventh Framework Programme (FP7/2007-2013) under grant agreement 270327 (CompLACS).
• dc.format.mimetype application/pdfca
• dc.identifier.citation Gómez V, Kappen HJ, Peters J, Neumann G. Policy search for path integral control. In: Calders T, Esposito F, Hüllermeier E, Meo R, editors. Machine learning and knowledge discovery in databases. European conference, ECML PKDD 2014; 2014 Sep 15-19; Nancy, France. [place unknown]: Springer; 2014. p. 482-97. DOI: 10.1007/978-3-662-44848-9_31
• dc.identifier.doi http://dx.doi.org/10.1007/978-3-662-44848-9_31
• dc.identifier.uri http://hdl.handle.net/10230/32501
• dc.language.iso eng
• dc.publisher Springerca
• dc.relation.ispartof Calders T, Esposito F, Hüllermeier E, Meo R, editors. Machine learning and knowledge discovery in databases. European conference, ECML PKDD 2014; 2014 Sep 15-19; Nancy, France. [place unknown]: Springer; 2014. p. 482-97.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/270327
• dc.rights © Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-662-44848-9_31
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Path integrals
• dc.subject.keyword Stochastic optimal control
• dc.subject.keyword Policy search
• dc.title Policy search for path integral controlca
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion