Exploration of Learning Process in Cooperative Hunting with Deep Reinforcement Learning

Abstract

Collective behavior is one of the most fundamental yet challenging phenomena in understanding of animal and even human groups. Previous studies of collective behavior in animal groups have often focused on one-time or short-term performance, largely missing the potential of these systems to learn or to undergo changes over time. To address this problem, we introduced a computational simulation environment based on multi-agent deep reinforcement learning. Here, we studied cooperative hunting, which is a typical example of collective behavior, and found that an individual originally playing a role suddenly changed its role to another at certain time point through learning when two predators cooperated to capture prey. On the other hand, when three predators cooperated, there was no such clear role specialization by individuals and their roles were interchanged more flexibly. Furthermore, we found that the proportion of successful predation can oscillate over time, regardless of role division and its consistency. These results complement existing findings established by observations in nature and provide insight for further understanding of collective animal behavior.