A neural model of hierarchical reinforcement learning.

We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time d...

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Main Authors: Daniel Rasmussen, Aaron Voelker, Chris Eliasmith
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2017-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5500327?pdf=render
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author Daniel Rasmussen
Aaron Voelker
Chris Eliasmith
author_facet Daniel Rasmussen
Aaron Voelker
Chris Eliasmith
author_sort Daniel Rasmussen
collection DOAJ
description We develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain's general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model's behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions.
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spelling doaj.art-a60fd229ec3b4e7e9b643d4b4ba2bc142022-12-22T01:16:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01127e018023410.1371/journal.pone.0180234A neural model of hierarchical reinforcement learning.Daniel RasmussenAaron VoelkerChris EliasmithWe develop a novel, biologically detailed neural model of reinforcement learning (RL) processes in the brain. This model incorporates a broad range of biological features that pose challenges to neural RL, such as temporally extended action sequences, continuous environments involving unknown time delays, and noisy/imprecise computations. Most significantly, we expand the model into the realm of hierarchical reinforcement learning (HRL), which divides the RL process into a hierarchy of actions at different levels of abstraction. Here we implement all the major components of HRL in a neural model that captures a variety of known anatomical and physiological properties of the brain. We demonstrate the performance of the model in a range of different environments, in order to emphasize the aim of understanding the brain's general reinforcement learning ability. These results show that the model compares well to previous modelling work and demonstrates improved performance as a result of its hierarchical ability. We also show that the model's behaviour is consistent with available data on human hierarchical RL, and generate several novel predictions.http://europepmc.org/articles/PMC5500327?pdf=render
spellingShingle Daniel Rasmussen
Aaron Voelker
Chris Eliasmith
A neural model of hierarchical reinforcement learning.
PLoS ONE
title A neural model of hierarchical reinforcement learning.
title_full A neural model of hierarchical reinforcement learning.
title_fullStr A neural model of hierarchical reinforcement learning.
title_full_unstemmed A neural model of hierarchical reinforcement learning.
title_short A neural model of hierarchical reinforcement learning.
title_sort neural model of hierarchical reinforcement learning
url http://europepmc.org/articles/PMC5500327?pdf=render
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