Journal of Jilin University (Information Science Edition) ›› 2018, Vol. 36 ›› Issue (4): 439-443.

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Application of Improved Q-Learning Algorithm in Path Planning

GAO Le,MA Tianlu,LIU Kai,ZHANG Yuxuan   

  1. College of Instrumentation and Electrical Engineering,Jilin University,Changchun 130012,China
  • Online:2018-07-24 Published:2019-01-18

Abstract: Aiming at the problem of low efficiency and slow learning in discrete state of Q-Learning algorithm.The improved algorithm adds a learning process on the basis of the original algorithm,and makes deep learning of the environment.An improved Q-Learning algorithm is proposed to simulate in grid environment. It has been successfully applied to the path planning of a mobile robot in a multi barrier environment,and the results prove the feasibility of the algorithm. The improved Q-Learning algorithm can converge faster,reduce the number of learning,and increase the efficiency by 20%. The framework of the algorithm has strong generality for solving the same kind of problems.

Key words: path planning, improved Q-Learning algorithm, reinforcement learning, grid method, robot

CLC Number: 

  • TP391