Journal of Astronautics ›› 2022, Vol. 43 ›› Issue (5): 685-695.doi: 10.3873/j.issn.1000-1328.2022.05.013

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Deep Reinforcement Learning Guidance Law for Intercepting Endo atmospheric Maneuvering Targets

QIU Xiaoqi, GAO Changsheng, JING Wuxing   

  1. Department of Aerospace Engineering, Harbin Institute of Technology, Harbin 150001, China
  • Received:2021-07-20 Revised:2021-10-22 Online:2022-05-15 Published:2022-05-15

Abstract: Aiming at the problem of intercepting endo atmospheric high speed maneuvering targets, a deep reinforcement learning guidance law is proposed based on the twin delayed deep deterministic policy gradient(TD3) algorithm. It directly maps the engagement information to the commanded acceleration of the interceptor, which is an end to-end, model free guidance strategy. Firstly, the engagement kinematic model of both sides is described as a Markov decision process suitable for deep reinforcement learning algorithms. After that, a complete deep reinforcement learning guidance algorithm is constructed by reasonably designing the engagement scenarios, action space, state space and network structure required for algorithm training. The reward shaping and random initialization are introduced to construct a complete algorithm. The simulation results show that, compared with the proportional guidance and augmented proportional guidance laws, the proposed guidance strategy can reduce the requirement for mid course guidance while having smaller miss distances. It has good robustness and generalization ability, with less computational burden that makes it eligible to run on missile borne computers.

Key words: Missile guidance, Endo atmospheric interception;Maneuvering target; Deep reinforcement learning; Markov decision

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