TY - GEN
T1 - Multi-agent Recurrent Actor-Critic for Cooperative Decision-Making in Within Visual Range Air Combat
AU - Chen, Can
AU - Yin, Dengyu
AU - Mo, Li
AU - Lv, Maolong
AU - Lin, Dan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - In recent years, the significance of cooperative decision-making in autonomous air combat scenarios has gained widespread recognition. Consequently, this paper introduces an innovative algorithm named Multi-Agent Recurrent Actor-Critic (MARAC), explicitly designed to enhance cooperative decision-making in autonomous within visual range (WVR) air combat. By leveraging the Centralized-Training-Distributed-Execution (CTDE) framework and utilizing recurrent neural networks, the MARAC algorithm improves the efficacy of communication-independent cooperative air combat strategies, resulting in more effective outcomes. Furthermore, the incorporation of curriculum learning (CL) and self-play (SP) techniques is proposed to boost the algorithm’s learning efficiency. Experimental results demonstrate that the MARAC algorithm significantly enhances the performance of cooperative decision-making by effectively addressing challenges associated with partial observations and complex confrontation dynamics.
AB - In recent years, the significance of cooperative decision-making in autonomous air combat scenarios has gained widespread recognition. Consequently, this paper introduces an innovative algorithm named Multi-Agent Recurrent Actor-Critic (MARAC), explicitly designed to enhance cooperative decision-making in autonomous within visual range (WVR) air combat. By leveraging the Centralized-Training-Distributed-Execution (CTDE) framework and utilizing recurrent neural networks, the MARAC algorithm improves the efficacy of communication-independent cooperative air combat strategies, resulting in more effective outcomes. Furthermore, the incorporation of curriculum learning (CL) and self-play (SP) techniques is proposed to boost the algorithm’s learning efficiency. Experimental results demonstrate that the MARAC algorithm significantly enhances the performance of cooperative decision-making by effectively addressing challenges associated with partial observations and complex confrontation dynamics.
KW - Autonoumous Air Combat
KW - Cooperative Decision-Making
KW - Multi-Agent Reinforcement Learning
KW - Recurrent Neural Network
UR - http://www.scopus.com/pages/publications/105000772841
U2 - 10.1007/978-981-96-2240-5_39
DO - 10.1007/978-981-96-2240-5_39
M3 - Conference contribution
AN - SCOPUS:105000772841
SN - 9789819622399
T3 - Lecture Notes in Electrical Engineering
SP - 392
EP - 401
BT - Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 11
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2024
Y2 - 9 August 2024 through 11 August 2024
ER -