A New Deep Reinforcement Learning Algorithm for UAV Swarm Confrontation Game

UAV swarm confrontation game is a type of intelligent game problem. Multi-agent reinforcement learning theory provides an effective solution for this game. However, when using common multi-agent deep reinforcement learning algorithms, such as the multi-agent deep deterministic policy gradient (MADDPG) algorithm, to train the strategy of UAV swarm, there are issues such as slow convergence speed and weak generalization ability on similar tasks. To address these issues, this paper combines the model-agnostic meta-learning (MAML) algorithm in few-shot learning with the original MADDPG algorithm, and proposes an improved MB-MADDPG algorithm, which is applied to the strategy optimization of a UAV swarm confrontation task. Experimental results show that compared with the original algorithm, the improved algorithm can accelerate the convergence while maintaining the training effect, and the success rate of defense after training with both algorithms exceeds 50%.