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

DOI10.1016/j.nahs.2006.12.001zbMath1169.68042OpenAlexW2025228809WikidataQ115039494 ScholiaQ115039494MaRDI QIDQ1003553

Marc Ricordeau, Michel Liquière

Publication date: 4 March 2009

Published in: Nonlinear Analysis. Hybrid Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.nahs.2006.12.001

zbMATH Keywords

Galois latticesreinforcement learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Knowledge representation (68T30)

Related Items (1)

A new algebraic structure for formal concept analysis

Cites Work

• Equivalence notions and model minimization in Markov decision processes
• Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
• \({\mathcal Q}\)-learning
• Relational reinforcement learning
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