Hybrid attention-oriented experience replay for deep reinforcement learning and its application to a multi-robot cooperative hunting problem

Abstract

Multiple robots complete a cooperative hunting task by obtaining environmental information and autonomously learning hunting decision-making strategies. However, with the increase in the number of environment participants, it becomes difficult for robots to process a large amount of environmental information. Thus, a multi-robot cooperative hunting decision-making method called hybrid attention-oriented experience replay in multi-agent deep deterministic policy gradient (HAER-MADDPG) is proposed. First, a hybrid attention module is designed to pay greater attention to key information in a large amount of environmental information by integrating it with the multi-agent deep deterministic policy gradient (MADDPG). The method then combines hybrid attention and prioritized experience replay to improve the utilization of experience samples. Finally, the proposed algorithm is tested through a predator–prey game. The results show that the effectiveness, convergence speed, and scalability of the proposed algorithm are better than those of the baseline algorithms. In addition, HAER-MADDPG is effectively applied to a hunting task with real robots.