Application of game learning system for fighter guidance

  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.