Diversity of behavioral strategy in cooperative hunting using multi-agent deep reinforcement learning

Abstract

Cooperative hunting is a widespread form of cooperation in nature, and it is known that the level of organization of this predation varies among species. However, how cooperative forms of predation have evolved and been maintained is not well understood. In this study, we addressed this issue using a multi-agent simulation based on deep reinforcement learning. We examined changes in behavioral strategies when changing factors that have been suggested to be associated with predation forms by previous observations in nature, and found that the highest level of organization with role division among individuals was emerged under the combined conditions of two factors: difficulty of prey capture, and food (reward) sharing. These results suggest that sophisticated predation forms, which have been thought to require high cognition, can evolve from relatively simple cognitive and learning mechanisms, and emphasize the close link between the predation form and the environment where the organism lives.