심층 강화학습을 이용한 시변 비례 항법 유도 기법Time-varying Proportional Navigation Guidance using Deep Reinforcement Learning

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In this paper, we propose a time-varying proportional navigation guidance law that determines the proportional navigation gain in real-time according to the operating situation. When intercepting a target, an unidentified evasion strategy causes a loss of optimality. To compensate for this problem, proper proportional navigation gain is derived at every time step by solving an optimal control problem with the inferred evader's strategy. Recently, deep reinforcement learning algorithms are introduced to deal with complex optimal control problem efficiently. We adapt the actor-critic method to build a proportional navigation gain network and the network is trained by the Proximal Policy Optimization(PPO) algorithm to learn an evasion strategy of the target. Numerical experiments show the effectiveness and optimality of the proposed method.
Publisher
한국군사과학기술학회
Issue Date
2020-08
Language
Korean
Citation

한국군사과학기술학회지, v.23, no.4, pp.399 - 406

ISSN
1598-9127
URI
http://hdl.handle.net/10203/281390
Appears in Collection
AE-Journal Papers(저널논문)
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