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Title: Probabilistic guided exploration for reinforcement learning in self-organizing neural networks
Authors: Wang, Peng
Zhou, Weigui Jair
Wang, Di
Tan, Ah-Hwee
Keywords: Reinforcement Learning
Self-organizing Neural Networks
Engineering::Computer science and engineering
Issue Date: 2018
Source: Wang, P., Zhou, W. J., Wang, D., & Tan, A.-H. (2018). Probabilistic guided exploration for reinforcement learning in self-organizing neural networks. 2018 IEEE International Conference on Agents (ICA). doi:10.1109/agents.2018.8460067
Conference: 2018 IEEE International Conference on Agents (ICA)
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 knowledge-based 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.
DOI: 10.1109/agents.2018.8460067
Schools: School of Computer Science and Engineering 
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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