Multi-UCAV Air Combat in Short-Range Maneuver Strategy Generation using Reinforcement Learning and Curriculum Learning

2020 ◽  
Author(s):  
Weiren Kong ◽  
Deyun Zhou ◽  
Kai Zhang ◽  
Zhen Yang ◽  
Wansha Yang
Keyword(s):  
Reinforcement Learning ◽  
Short Range ◽  
Air Combat

scholarly journals Maneuver Decision of UAV in Short-Range Air Combat Based on Deep Reinforcement Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 363-378 ◽  
Author(s):  
Qiming Yang ◽  
Jiandong Zhang ◽  
Guoqing Shi ◽  
Jinwen Hu ◽  
Yong Wu
Keyword(s):  
Reinforcement Learning ◽  
Short Range ◽  
Air Combat

scholarly journals UAV cooperative air combat maneuver decision based on multi-agent reinforcement learning

2021 ◽  
Vol 32 (6) ◽  
pp. 1421-1438
Author(s):  
Zhang Jiandong ◽  
Yang Qiming ◽  
Shi Guoqing ◽  
Lu Yi ◽  
Wu Yong
Keyword(s):  
Reinforcement Learning ◽  
Air Combat ◽  
Multi Agent

scholarly journals Research on Air Combat Maneuver Decision-Making Method Based on Reinforcement Learning

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 279 ◽  
Author(s):  
Xianbing Zhang ◽  
Guoqing Liu ◽  
Chaojie Yang ◽  
Jiang Wu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Signal Design ◽  
Training Environment ◽  
Strategy Space ◽  
Intelligent Decision Making ◽  
Combat Training ◽  
Network Method ◽  
Air Combat

With the development of information technology, the degree of intelligence in air combat is increasing, and the demand for automated intelligent decision-making systems is becoming more intense. Based on the characteristics of over-the-horizon air combat, this paper constructs a super-horizon air combat training environment, which includes aircraft model modeling, air combat scene design, enemy aircraft strategy design, and reward and punishment signal design. In order to improve the efficiency of the reinforcement learning algorithm for the exploration of strategy space, this paper proposes a heuristic Q-Network method that integrates expert experience, and uses expert experience as a heuristic signal to guide the search process. At the same time, heuristic exploration and random exploration are combined. Aiming at the over-the-horizon air combat maneuver decision problem, the heuristic Q-Network method is adopted to train the neural network model in the over-the-horizon air combat training environment. Through continuous interaction with the environment, self-learning of the air combat maneuver strategy is realized. The efficiency of the heuristic Q-Network method and effectiveness of the air combat maneuver strategy are verified by simulation experiments.

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