Heuristic Q-learning based on experience replay for three-dimensional path planning of the unmanned aerial vehicle.

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ID: 96681
2019
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Abstract
In order to solve the problem that the existing reinforcement learning algorithm is difficult to converge due to the excessive state space of the three-dimensional path planning of the unmanned aerial vehicle, this article proposes a reinforcement learning algorithm based on the heuristic function and the maximum average reward value of the experience replay mechanism. The knowledge of track performance is introduced to construct heuristic function to guide the unmanned aerial vehicles' action selection and reduce the useless exploration. Experience replay mechanism based on maximum average reward increases the utilization rate of excellent samples and the convergence speed of the algorithm. The simulation results show that the proposed three-dimensional path planning algorithm has good learning efficiency, and the convergence speed and training performance are significantly improved.
Reference Key
xie2019heuristicscience Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xie, Ronglei;Meng, Zhijun;Zhou, Yaoming;Ma, Yunpeng;Wu, Zhe;
Journal Science progress
Year 2019
DOI
10.1177/0036850419879024
URL
Keywords
q-learning path planning unmanned aerial vehicle experience replay heuristic information

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