Collaborative hunting in artificial agents with deep reinforcement learning
Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed as an advanced hunting strategy requiring large brains that involve high level cognition. However, recent findings that collaborative hunting have also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes using computational multi-agent simulation based on deep reinforcement learning. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between observations and actions via distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our results of computational ecology emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.