Open-Ended Learning: A Conceptual Framework Based on Representational Redescription

Reinforcement learning (RL) aims at building a policy that maximizes a task-related reward within a given domain. When the domain is known, i.e., when its states, actions and reward are defined, Markov Decision Processes (MDPs) provide a convenient theoretical framework to formalize RL. But in an op...

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Detalles Bibliográficos
Autores principales: Doncieux, Stephane, Filliat, David, Díaz-Rodríguez, Natalia, Hospedales, Timothy, Duro, Richard, Coninx, Alexandre, Roijers, Diederik M., Girard, Benoît, Perrin, Nicolas, Sigaud, Olivier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167466/
https://www.ncbi.nlm.nih.gov/pubmed/30319388
http://dx.doi.org/10.3389/fnbot.2018.00059

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167466/
https://www.ncbi.nlm.nih.gov/pubmed/30319388
http://dx.doi.org/10.3389/fnbot.2018.00059

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