Algebraic results and bottom-up algorithm for policies generalization in reinforcement learning using concepts lattices

Marc Ricordeau; Michel Liquière

Conference Papers Year : 2006

Algebraic results and bottom-up algorithm for policies generalization in reinforcement learning using concepts lattices

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Marc Ricordeau

Function : Author
PersonId : 938479

Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier

Michel Liquière

Function : Author
PersonId : 938480

Agents, Apprentissage, Contraintes

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Data Structures and Algorithms [cs.DS]

Isabelle Gouat : Connect in order to contact the contributor

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00378912

Submitted on : Monday, April 27, 2009-12:07:26 PM

Last modification on : Friday, March 24, 2023-2:52:51 PM

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lirmm-00378912 , version 1 (27-04-2009)

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HAL Id : lirmm-00378912 , version 1

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Marc Ricordeau, Michel Liquière. Algebraic results and bottom-up algorithm for policies generalization in reinforcement learning using concepts lattices. ICHSA'06: International Conference on Hybrid Systems and Applications, Lafayette, LA, USA, pp.N/A. ⟨lirmm-00378912⟩

Algebraic results and bottom-up algorithm for policies generalization in reinforcement learning using concepts lattices

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