Multi Agent Deep Deterministic Policy Gradient Algorithm Based on Multi-Head Attention Mechanism and Prioritized Experience Replay

This paper proposes a multi-agent deep deterministic policy gradient (MADDPG) algorithm that integrates multi-head attention mechanism and prioritized experience replay mechanism, aiming to improve the collaborative decision-making ability of multi-agent systems in complex dynamic environments. This algorithm introduces a multi-head attention mechanism to enable agents to effectively focus on key information and tasks, while utilizing a prioritized experience replay mechanism to optimize the learning process and improve sample utilization. To verify the effectiveness of the algorithm, we conducted experiments on the National Chess deduction competition platform. The results show that compared with classic algorithms such as QMIX and CommNet, the algorithm proposed in this paper exhibits significant advantages in task completion rate, average reward, and other aspects. In addition, the algorithm also exhibits good lightweight characteristics in terms of computational efficiency and resource consumption, providing new ideas and methods for the application of multi-agent reinforcement learning in complex tasks.