Application of game learning system for fighter guidance

Wei TAO

doi:10.19693/j.issn.1673-3185.01937

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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

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

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Application of game learning system for fighter guidance

Wei TAO

China Ship Development and Design Center, Wuhan 430064, China

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Received Date: April 27, 2020
Revised Date: June 16, 2020
Official website online publication date: December 07, 2020

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.

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Abstract

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.
- early warning aircraft (EWA),
- fighter guidance,
- deep reinforcement learning,
- game

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