Algebraic results and bottom-up algorithm for policies generalization in reinforcement learning using concept lattices
type: Item
instance of: scholarly article
title: Algebraic results and bottom-up algorithm for policies generalization in reinforcement learning using concept lattices
zbMATH Open document ID: 1169.68042
author: Marc Ricordeau
author: Michel Liquière
published in: Nonlinear Analysis. Hybrid Systems
publication date: 2009-03-04T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68T30
zbMATH DE Number: 5523028
zbMATH Keywords: reinforcement learning
zbMATH Keywords: Galois lattices
Wikidata QID: Q115039494
MaRDI profile type: Publication
full work available at URL: j.nahs.2006.12.001
OpenAlex ID: W2025228809
cites work: \({\mathcal Q}\)-learning
cites work: Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
cites work: Equivalence notions and model minimization in Markov decision processes
cites work: Q4527272
cites work: Relational reinforcement learning
cites work: Q4230685
cites work: Q4398828
DOI: 10.1016/J.NAHS.2006.12.001
description: scientific article; zbMATH DE number 5523028