一种深度强化学习制导控制一体化算法

Pei Pei, Shao Ming He*, Jiang Wang, De Fu Lin

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

26 引用 (Scopus)

摘要

This paper proposes an integrated guidance and control algorithm based on deep reinforcement learning technique. Differently from the traditional integrated guidance and control algorithm and designing the guidance loop and control loop separately, the fin deflection command of proposed integrated guidance and control algorithm is given by the agent through the observation states of missile. The agent is generated by the deep reinforcement learning. To utilize the deep reinforcement learning technique in integrated guidance and control problem, we transfer the integrated guidance and control problem into a Markovian decision process that enables the application of reinforcement learning theory. A heuristic way is utilized to shape a proper reward function that has tradeoff between guidance accuracy, energy consumption and interception time. The state-of-the-art deep deterministic policy gradient algorithm is utilized to learn an action policy that maps the observation states to a fin deflection command. Extensive empirical numerical simulations are performed to validate the effectiveness and robustness of proposed integrated guidance and control algorithm.

投稿的翻译标题Integrated Guidance and Control for Missile Using Deep Reinforcement Learning
源语言繁体中文
页(从-至)1293-1304
页数12
期刊Yuhang Xuebao/Journal of Astronautics
42
10
DOI
出版状态已出版 - 30 10月 2021

关键词

  • Deep deterministic policy gradient
  • Deep reinforcement learning
  • Heuristic learning
  • Integrated guidance and control
  • Zero-effort-miss

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