Self-organizing neural architecture for reinforcement learning

Self-organizing neural architecture for reinforcement learning

Self-organizing neural networks are typically associated with unsupervised learning. This paper presents a self-organizing neural architecture, known as TD-FALCON, that learns cognitive codes across multi-modal pattern spaces, involving sensory input, actions, and rewards, and is capable of adapting...

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Main Author: TAN, Ah-hwee
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2006
Subjects:
Databases and Information Systems
Online Access:https://ink.library.smu.edu.sg/sis_research/6833
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Institution: Singapore Management University
Language: English
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spelling sg-smu-ink.sis_research-78362022-01-27T03:48:03Z Self-organizing neural architecture for reinforcement learning TAN, Ah-hwee Self-organizing neural networks are typically associated with unsupervised learning. This paper presents a self-organizing neural architecture, known as TD-FALCON, that learns cognitive codes across multi-modal pattern spaces, involving sensory input, actions, and rewards, and is capable of adapting and functioning in a dynamic environment with external evaluative feedback signals. We present a case study of TD-FALCON on a mine avoidance and navigation cognitive task, and illustrate its performance by comparing with a state-of-the-art reinforcement learning approach based on gradient descent backpropagation algorithm 2006-05-28T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/6833 info:doi/10.1007/11759966_70 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
TAN, Ah-hwee
Self-organizing neural architecture for reinforcement learning
description Self-organizing neural networks are typically associated with unsupervised learning. This paper presents a self-organizing neural architecture, known as TD-FALCON, that learns cognitive codes across multi-modal pattern spaces, involving sensory input, actions, and rewards, and is capable of adapting and functioning in a dynamic environment with external evaluative feedback signals. We present a case study of TD-FALCON on a mine avoidance and navigation cognitive task, and illustrate its performance by comparing with a state-of-the-art reinforcement learning approach based on gradient descent backpropagation algorithm
format text
author TAN, Ah-hwee
author_facet TAN, Ah-hwee
author_sort TAN, Ah-hwee
title Self-organizing neural architecture for reinforcement learning
title_short Self-organizing neural architecture for reinforcement learning
title_full Self-organizing neural architecture for reinforcement learning
title_fullStr Self-organizing neural architecture for reinforcement learning
title_full_unstemmed Self-organizing neural architecture for reinforcement learning
title_sort self-organizing neural architecture for reinforcement learning
publisher Institutional Knowledge at Singapore Management University
publishDate 2006
url https://ink.library.smu.edu.sg/sis_research/6833
_version_ 1770576078082932736

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