@inproceedings{6a6f85c02aea4ac1b68532997ea94178,
title = "Time-and-Angle-Constrained Cooperative Guidance Based on Reinforcement Learning",
abstract = "Time-and-angle-constrained cooperative guidance based on reinforcement learning is developed to address the problem of cooperative guidance. Firstly, time-constrained guidance law and angle-constrained guidance law are designed separately, and the calculation methods for cooperative time and angle are designed. Time-and-angle-constrained cooperative guidance law is obtained by a weight coefficient. Secondly, reinforcement learning is used to optimize the coefficient. A simple observation variable set is created. Deep deterministic policy gradient (DDPG) algorithm is applied to agents, as well as network structure and reward are designed. Thirdly, agents are trained and the coefficient of cooperative guidance law can be output by trained agents. Comparative simulation is conducted to verify the performance of the guidance law.",
keywords = "angle constraint, cooperative guidance, reinforcement learning, time constraint",
author = "Changhai Wang and Jianglong Yu and Xiwang Dong and Qingdong Li and Zhang Ren",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023 ; Conference date: 24-11-2023 Through 27-11-2023",
year = "2024",
doi = "10.1007/978-981-97-3340-8\_29",
language = "英语",
isbn = "9789819733392",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "317--329",
editor = "Guo-Ping Jiang and Mengyi Wang and Zhang Ren",
booktitle = "Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control - Swarm Guidance Technologies",
address = "德国",
}