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Permanent link (DOI): https://doi.org/10.7939/R3MD8M

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A Computational Model of Learning from Replayed Experience in Spatial Navigation Open Access

Descriptions

Other title
Subject/Keyword
Experience replay
Reinforcement learning
Spatial navigation
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Mirian HosseinAbadi, MahdiehSadat
Supervisor and department
Sutton, Rich (Computing Science)
Elio, Renee (Computing Science)
Examining committee member and department
Schuurmans, Dale (Computing Science)
Spetch, Marcia (Psychology)
Ludvig, Elliot ( Princeton Neuroscience Institute, Princeton University)
Department
Department of Computing Science
Specialization

Date accepted
2011-11-28T21:53:49Z
Graduation date
2012-06
Degree
Master of Science
Degree level
Master's
Abstract
In this thesis we propose a computational model of animal behavior in spatial navigation, based on reinforcement learning ideas. In the field of computer science and specifically artificial intelligence, replay refers to retrieving and reprocessing the experiences that are stored in an abstract representation of the environment. Our model uses the replay idea that existed separately in both computer science and neuroscience. In neuroscience, it refers to the reactivation of neurons in the hippocampus that were previously active during a learning task, in such a way that can be interpreted as replaying previous experiences. Therefore, it is natural to use RL algorithms to model the biological replay phenomena. We illustrated, through computational experiments, that our replay model can explain many previously hard-to-explain behavioral navigational experiments such as latent learning or insight experiments. There have been many computational models proposed to model rats behavior in mazes or open field environments. We showed that our model has two major advantages over prior ones: (i) The learning algorithm used in our model is simpler than that of previous computational models, yet capable of explaining complicated behavioral phenomena in spatial navigation. (ii) our model generates different replay sequences that are consistent with replay patterns observed in the neural experiments on the rat brain.
Language
English
DOI
doi:10.7939/R3MD8M
Rights
License granted by MahdiehSadat Mirian HosseinAbadi (mirianho@ualberta.ca) on 2011-11-28T12:01:48Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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