Autonomous Cooperative Hunting with Rule-Based and Self-Learning Control for Multiagent Systems

Abstract

This paper considers the problem of autonomous cooperative hunting in an unknown dynamic environment, where a group of mobile agents collaborate to capture a moving target. Due to the decentralized decision-making nature of multi-agent systems and the presence of real-world constraints, it is a challenging task. To solve this problem, an artificial rule based hunting algorithm (AR-HA) is firstly developed based on the principles of attraction and repulsion with heading adjustment, and each agent is controlled by the designed rules. Then, to further enhance the stability of cooperative hunting, a self-learning algorithm based on Twin Delayed Deep Deterministic policy gradient (SL-TD3) is proposed. Each agent is governed by its own SL-TD3 controller and learns independently from its interaction with the environment, taking advantage of the reward function designed based on the control rules of AR-HA. Besides, in order to improve training efficiency, imitation learning is employed to initialize the actor network. Experiments on both virtual and real robots demonstrate the effectiveness of the proposed algorithms for autonomous cooperative hunting.