Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2018

Abstract

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.

Keywords

Reinforcement learning, self-organizing neural networks, guided exploration

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

Proceedings of 2018 IEEE International Conference on Agents, ICA, Singapore, July 28-31

First Page

109

Last Page

112

Identifier

10.1109/AGENTS.2018.8460067

Publisher

IEEE

City or Country

Singapore

Additional URL

https://doi.org/10.1109/AGENTS.2018.8460067

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