@inproceedings{c1310bec8d5f4e8d8cc54e1de4c25851,
title = "Multi-UCAV Air Combat in Short-Range Maneuver Strategy Generation using Reinforcement Learning and Curriculum Learning",
abstract = "We present an approach for learning a reactive maneuver strategy for a UCAV formation involved in a short-range multi-UCAV air combat engagement. Specifically, we define an efficient state representation, which breaks down the complexity caused by the large state space in a multi-UCAV air combat engagement. Then a parameter sharing dueling deep Q-network (PS-DDQN) algorithm is proposed to train the UCAV formation. The learning reactive maneuver strategy is shared among our UCAVs to encourage cooperative behaviors. In addition, curriculum learning and self-play extend the maneuver strategy to more difficult scenarios. Thus, speeding up the training process and improving the learning effect. Finally, the effectiveness of the algorithm and the intelligence degree of maneuver strategy is verified by the simulation test of convergence and maneuver strategy quality.",
keywords = "air combat, curriculum learning, Multi-UCAV, reinforcement learning, training simulations",
author = "Weiren Kong and Deyun Zhou and Kai Zhang and Zhen Yang and Wansha Yang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 ; Conference date: 14-12-2020 Through 17-12-2020",
year = "2020",
month = dec,
doi = "10.1109/ICMLA51294.2020.00238",
language = "英语",
series = "Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1174--1181",
editor = "Wani, {M. Arif} and Feng Luo and Xiaolin Li and Dejing Dou and Francesco Bonchi",
booktitle = "Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020",
}