The interception game between groups of unmanned aerial vehicles (UAVs) is crucial in the future intelligent warfare. In response to the collaborative interception gaming problem against aerial cluster attacks, a multi-agent deep reinforcement learning (DRL) framework based on the twin delayed deep deterministic policy gradient (TD3) method is proposed. The framework combines single-agent delayed policy gradient algorithms with a centralized evaluation and distributed execution algorithm architecture. In order to enhance the convergence of the algorithm, a generalized advantage function is designed. The simulation results show that the strategy enables UAVs to assign interception targets based on real-time battlefield conditions intelligently.
A Multi-agent Reinforcement Learning Framework for Coordinated Multi-UAV Interception Strategies
Lect. Notes Electrical Eng.
International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024
2025-03-06
11 pages
Article/Chapter (Book)
Electronic Resource
English