TY - GEN
T1 - UAV Swarm Confrontation Based on Multi-Agent Soft Actor-Critic Method
AU - Jiao, Yongkang
AU - Fu, Wenxing
AU - Cao, Xinying
AU - Wang, Yaping
AU - Xu, Pengfei
AU - Wang, Yusheng
AU - Yu, Lanlin
AU - Du, Haibo
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The UAV swarm confrontation learning environment faces complex challenges due to its high dimensionality, nonlinearity, incomplete information, and continuous action space.To address these issues, this study proposes a multi-agent soft actor-critic (MASAC) deep reinforcement learning method based on incomplete information. Built on the centralized training-distributed execution (CTDE) framework,the proposed method establishes a UAV swarm confrontation game model and simulates a multi-UAV combat environment in continuous space. Simulation results demonstrate that the MASAC method outperforms existing multi-agent deep reinforcement learning techniques in terms of convergence speed and stability. The results of this study under score the practicality and effectiveness of the MASAC method in enabling intelligent decision-making for UAV swarms, thereby offering essential technical support for future advancements in UAV operations.
AB - The UAV swarm confrontation learning environment faces complex challenges due to its high dimensionality, nonlinearity, incomplete information, and continuous action space.To address these issues, this study proposes a multi-agent soft actor-critic (MASAC) deep reinforcement learning method based on incomplete information. Built on the centralized training-distributed execution (CTDE) framework,the proposed method establishes a UAV swarm confrontation game model and simulates a multi-UAV combat environment in continuous space. Simulation results demonstrate that the MASAC method outperforms existing multi-agent deep reinforcement learning techniques in terms of convergence speed and stability. The results of this study under score the practicality and effectiveness of the MASAC method in enabling intelligent decision-making for UAV swarms, thereby offering essential technical support for future advancements in UAV operations.
KW - deep reinforcement learning
KW - game theory
KW - multi-agent systems
UR - http://www.scopus.com/inward/record.url?scp=85218023611&partnerID=8YFLogxK
U2 - 10.1109/ICUS61736.2024.10839859
DO - 10.1109/ICUS61736.2024.10839859
M3 - 会议稿件
AN - SCOPUS:85218023611
T3 - Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024
SP - 878
EP - 883
BT - Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024
A2 - Song, Rong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Unmanned Systems, ICUS 2024
Y2 - 18 October 2024 through 20 October 2024
ER -