Chinese Journal of Intelligent Science and Technology ›› 2020, Vol. 2 ›› Issue (4): 354-360.doi: 10.11959/j.issn.2096-6652.202038

• Special Issue: Deep Reinforcement Learning • Previous Articles     Next Articles

Depth control of autonomous underwater vehicle using deep reinforcement learning

Rizhong WANG, Huiping LI, Di CUI, Demin XU   

  1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
  • Revised:2020-11-30 Online:2020-12-15 Published:2020-12-01
  • Supported by:
    The National Natural Science Foundation of China(61922068);The National Natural Science Foundation of China(61733014);The Science Foundation for Distinguished Young Scholars of Shaanxi Province(2019JC-14);Northwestern Polytechnical University Aoxiang Youth Scholar Program(20GH0201111)


The depth control problem of autonomous underwater vehicle (AUV) by using deep reinforcement learning method was mainly studied.Different from the traditional control algorithm, the deep reinforcement learning method allows the AUV to learn the control law independently, avoiding the artificial establishment of accurate model and design control law.The deep deterministic policy gradient method was used to design two neural networks: actor and critic.Actor neural network enabled agents to make corresponding control actions.Critic neural network was used to estimate the action-value function in reinforcement learning.The AUV depth control was conducted by training of actor and critic neural networks.The effectiveness of the algorithm was proved by simulation on OpenAI Gym.

Key words: autonomous underwater vehicle, depth control, deep reinforcement learning

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