Collaborative Penetration Algorithm with Dominant Region Analysis Embedded in Deep Reinforcement Learning

The problem of UAV attack-defense confrontation is a hot research direction in the field of unmanned systems at present. However, in an environment with threat areas or obstacles, when the enemy is a defender with higher mobility or pursuit ability, the cooperative penetration problem of multiple UAVs is still lack of effective solutions. Therefore, this paper combines the theoretical analysis of game theory and the advantages of reinforcement learning in complex scenes, and designs an algorithm framework for embedding dominant region analysis into deep reinforcement learning. On the premise of sacrificing strategy, we analytically derive the attacker's dominance region through geometric optimization and integrate this framework into the Deep Deterministic Policy Gradient (DDPG) algorithm by enhancing state space formulation, reward function design, and termination criteria. Numerical simulations demonstrate the algorithm's superior efficacy over baseline reinforcement learning approaches, exhibiting reduced training time (42.8%), increased penetration success rate (31%), and optimized trajectory lengths (4.9%).