Chinese Journal of Intelligent Science and Technology ›› 2022, Vol. 4 ›› Issue (3): 418-425.doi: 10.11959/j.issn.2096-6652.202244

• Papers and Reports • Previous Articles     Next Articles

A path planning method for complex naval battle field based on an improved DQN algorithm

Zhou YU1, Jing BI1, Haitao YUAN2   

  1. 1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    2 School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
  • Revised:2022-08-15 Online:2022-09-15 Published:2022-09-01
  • Supported by:
    The National Natural Science Foundation of China(62073005);The National Natural Science Foundation of China(62173013)

Abstract:

To solve a target tracking problem of multiple warships in a sea battlefield environment, the multiple agents (warships) were focused on, and an improved deep Q-network (DQN) algorithm was proposed.It considers the characteristics of a multi-agent reinforcement learning environment based on a traditional DQN algorithm.It adds a network with the same structure and different parameters and updates a Q actual value and a Q estimated one, respectively to realize convergence of a value function.Besides, it adopts a mechanism of experience playback and an update of one of two parameters for a target network to effectively solve problems of high training errors of neural networks, poor generalization ability, and unstable training.Experimental results demonstrate that compared with the traditional DQN algorithm, the improved DQN one adapts to different complex and dynamical sea battlefield environments in a faster manner, and the ability to avoid obstacles has more than doubled and larger training reward.

Key words: deep Q network, reinforcement learning, multi-agent, path planning, target tracking

CLC Number: 

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