Generalization strategy design of UAVs pursuit evasion game based on DDPG

Abstract

UAVs pursuit evasion game is a research hotspot in the field of air combat. Traditional solutions have many limitations to this problem, such as the difficulty of the model to adapt to complex dynamic environments to quickly make decisions, and the poor generalization of different mission scenarios. Based on the DDPG(deep deterministic policy gradient) algorithm, a mathematical model of UAVs pursuit and evasion countermeasures is established in this paper. On this basis, this research designs a variety of countermaneuver strategies for escaping UAV, and uses the training method of course learning ideas. In the training process, the intelligence of the escaping UAV is gradually improved, so as to progressively train the confrontation strategy of the chasing UAV. The simulation results show that compared with direct training, the pursuit strategy of the chasing UAV trained by the research method of course learning can converge faster, and can better perform the hunting mission of enemy aircraft, and can be applied to a variety of enemy aircraft with a variety of maneuvering strategies, which effectively improved the generalization of the UAV′s pursuit and escape confrontation decision model.

Keywords

• uav
• pursuit-evasion game
• deep reinforcement learning
• ddpg
• curriculum learning