Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/349551
Type: Artigo
Title: A neuroscience inspired gated learning action selection mechanism
Author: Raizer, Klaus
Gudwin, Ricardo Ribeiro
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
Subject: Redes neurais (Computação)
Aprendizado de máquina
Algoritmos genéticos
Country: Países Baixos
Editor: Elsevier
Rights: Fechado
Identifier DOI: 10.1016/j.bica.2014.11.016
Address: https://www.sciencedirect.com/science/article/pii/S2212683X14000796
Date Issue: 2015
Appears in Collections:FEEC - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
000352744600007.pdf1.39 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.