Probabilistic guided exploration for reinforcement learning in self-organizing neural networks

Exploration is essential in reinforcement learning, which expands the search space of potential solutions to a given problem for performance evaluations. Specifically, carefully designed exploration strategy may help the agent learn faster by taking the advantage of what it has learned previously. H...

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Main Authors:	WANG, Peng, ZHOU, Weigui Jair, WANG, Di, TAN, Ah-hwee
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2018
Subjects:	Reinforcement learning self-organizing neural networks guided exploration Databases and Information Systems OS and Networks
Online Access:	https://ink.library.smu.edu.sg/sis_research/5467 https://ink.library.smu.edu.sg/context/sis_research/article/6470/viewcontent/ICA2018MineField.pdf
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Institution:	Singapore Management University
Language:	English

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spelling	sg-smu-ink.sis_research-64702020-12-24T03:01:37Z Probabilistic guided exploration for reinforcement learning in self-organizing neural networks WANG, Peng ZHOU, Weigui Jair WANG, Di TAN, Ah-hwee Exploration is essential in reinforcement learning, which expands the search space of potential solutions to a given problem for performance evaluations. Specifically, carefully designed exploration strategy may help the agent learn faster by taking the advantage of what it has learned previously. However, many reinforcement learning mechanisms still adopt simple exploration strategies, which select actions in a pure random manner among all the feasible actions. In this paper, we propose novel mechanisms to improve the existing knowledgebased exploration strategy based on a probabilistic guided approach to select actions. We conduct extensive experiments in a Minefield navigation simulator and the results show that our proposed probabilistic guided exploration approach significantly improves the convergence rate. 2018-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5467 info:doi/10.1109/AGENTS.2018.8460067 https://ink.library.smu.edu.sg/context/sis_research/article/6470/viewcontent/ICA2018MineField.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Reinforcement learning self-organizing neural networks guided exploration Databases and Information Systems OS and Networks
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Reinforcement learning self-organizing neural networks guided exploration Databases and Information Systems OS and Networks
spellingShingle	Reinforcement learning self-organizing neural networks guided exploration Databases and Information Systems OS and Networks WANG, Peng ZHOU, Weigui Jair WANG, Di TAN, Ah-hwee Probabilistic guided exploration for reinforcement learning in self-organizing neural networks
description	Exploration is essential in reinforcement learning, which expands the search space of potential solutions to a given problem for performance evaluations. Specifically, carefully designed exploration strategy may help the agent learn faster by taking the advantage of what it has learned previously. However, many reinforcement learning mechanisms still adopt simple exploration strategies, which select actions in a pure random manner among all the feasible actions. In this paper, we propose novel mechanisms to improve the existing knowledgebased exploration strategy based on a probabilistic guided approach to select actions. We conduct extensive experiments in a Minefield navigation simulator and the results show that our proposed probabilistic guided exploration approach significantly improves the convergence rate.
format	text
author	WANG, Peng ZHOU, Weigui Jair WANG, Di TAN, Ah-hwee
author_facet	WANG, Peng ZHOU, Weigui Jair WANG, Di TAN, Ah-hwee
author_sort	WANG, Peng
title	Probabilistic guided exploration for reinforcement learning in self-organizing neural networks
title_short	Probabilistic guided exploration for reinforcement learning in self-organizing neural networks
title_full	Probabilistic guided exploration for reinforcement learning in self-organizing neural networks
title_fullStr	Probabilistic guided exploration for reinforcement learning in self-organizing neural networks
title_full_unstemmed	Probabilistic guided exploration for reinforcement learning in self-organizing neural networks
title_sort	probabilistic guided exploration for reinforcement learning in self-organizing neural networks
publisher	Institutional Knowledge at Singapore Management University
publishDate	2018
url	https://ink.library.smu.edu.sg/sis_research/5467 https://ink.library.smu.edu.sg/context/sis_research/article/6470/viewcontent/ICA2018MineField.pdf
_version_	1770575468817285120

Probabilistic guided exploration for reinforcement learning in self-organizing neural networks

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