Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/241699
Type: Artigo de periódico
Title: A Neuroscience Inspired Gated Learning Action Selection Mechanism
Author: Raizer
Klaus; Gudwin
Ricardo R.
Abstract: This paper presents an algorithm for action selection, in the context of intelligent agents, capable of learning from rewards which are sparse in time. Inspiration for the proposed algorithm was drawn from computational neuroscience models of how the human prefrontal cortex (PFC) works. We have observed that this abstraction provides some advantages, such as the representation of solutions as trees, making it human-readable, and turning the learning process into a combinatorial optimization problem. Results for it solving the 1-2-AX working memory task are presented and discussed. We also argue the pros and cons of the proposed algorithm and, finally, address potential future work. (C) 2014 Elsevier B.V. All rights reserved.
Subject: Computational Models
Reinforcement
Ganglia
Country: AMSTERDAM
Editor: ELSEVIER SCIENCE BV
Citation: A Neuroscience Inspired Gated Learning Action Selection Mechanism. Elsevier Science Bv, v. 11, p. 65-74 JAN-2015.
Rights: embargo
Identifier DOI: 10.1016/j.bica.2014.11.016
Address: http://www.sciencedirect.com/science/article/pii/S2212683X14000796
Date Issue: 2015
Appears in Collections:Unicamp - Artigos e Outros Documentos

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