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Asynchronous reinforcement learning algorithms for solving discrete space path planning problems

  • Xingyu Zhao
  • Shifei Ding
  • Yuexuan An
  • Weikuan Jia
Article
  • 51 Downloads

Abstract

Reinforcement learning has great potential in solving practical problems, but when combining it with neural networks to solve small scale discrete space problems, it may easily trap in a local minimum value. Traditional reinforcement learning utilizes continuous updating of a single agent to learn policies, which easily leads to a slow convergence speed. In order to solve the above problems, we combine asynchronous methods with existing tabular reinforcement learning algorithms, propose a parallel architecture to solve the discrete space path planning problem, and present some new variants of asynchronous reinforcement learning algorithms. We apply these algorithms on the standard reinforcement learning environment problems, and the experimental results show that these methods can solve discrete space path planning problems efficiently. One of these algorithms, Asynchronous Phased Dyna-Q, which surpasses existing asynchronous reinforcement learning algorithms, can well balance exploration and exploitation.

Keywords

Reinforcement learning Path planning Dyna architecture Asynchronous methods Discrete space 

Notes

Acknowledgments

This work is supported by the Fundamental Research Funds for the Central Universities(No.2017XKZD03).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Xingyu Zhao
    • 1
  • Shifei Ding
    • 1
  • Yuexuan An
    • 1
  • Weikuan Jia
    • 2
  1. 1.School of Computer Science and TechnologyChina University of Mining and TechnologyXuzhouChina
  2. 2.School of Information Science and EngineeringShandong Normal UniversityJinanChina

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