Reinforcement Learning Model With Dynamic State Space Tested on Target Search Tasks for Monkeys: Self-Determination of Previous States Based on Experience Saturation and Decision Uniqueness

The real world is essentially an indefinite environment in which the probability space, i. e., what can happen, cannot be specified in advance. Conventional reinforcement learning models that learn under uncertain conditions are given the state space as prior knowledge. Here, we developed a reinforc...

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Detalles Bibliográficos
Autores principales: Katakura, Tokio, Yoshida, Mikihiro, Hisano, Haruki, Mushiake, Hajime, Sakamoto, Kazuhiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855153/
https://www.ncbi.nlm.nih.gov/pubmed/35185502
http://dx.doi.org/10.3389/fncom.2021.784592

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855153/
https://www.ncbi.nlm.nih.gov/pubmed/35185502
http://dx.doi.org/10.3389/fncom.2021.784592

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