TAO W. Application of game learning system for fighter guidance[J]. Chinese Journal of Ship Research, 2020, 15(Supp 1): 166–172. DOI: 10.19693/j.issn.1673-3185.01937
Citation: TAO W. Application of game learning system for fighter guidance[J]. Chinese Journal of Ship Research, 2020, 15(Supp 1): 166–172. DOI: 10.19693/j.issn.1673-3185.01937

Application of game learning system for fighter guidance

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  • Received Date: April 27, 2020
  • Revised Date: June 16, 2020
  • Available Online: December 07, 2020
© 2020 The Authors. Published by Editorial Office of Chinese Journal of Ship Research. Creative Commons License
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  •   Objectives  In order to explore the guidance strategy of early warning aircraft (EWA) for fighters through deep reinforcement learning, a game learning system for fighter guidance based on EWA is presented.
      Methods  The game learning system includes a deep learning reinforcement agent, battleground simulation system which can interact with the agent, game management system and distributed training system. For reinforcement learning requires significant interaction with the environment, a distributed training system is introduced to the self-training game platform to improve training efficiency. In the distributed system, the new mechanisms include decoupling the Learner and Actor, the periodic sharing of update gradients among training learners, and selecting the best agents while eliminating invalid agents.
      Results   Through the game learning system, a better EWA guidance strategy can be obtained after games between the blue agent and red agent, thereby enhancing the guidance operational capability of EWA.
      Conclusions  This paper provides a reference for improving the guidance combat capability of early warning aircraft.
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