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The comprehension of how local interactions arise in global collectivebehavior is of utmost importance in both biological and physical research.Traditional agent-based models often rely on static rules that fail to capturethe dynamic strategies of the biological world. Reinforcement learning has beenproposed as a solution, but most previous methods adopt handcrafted rewardfunctions that implicitly or explicitly encourage the emergence of swarmingbehaviors. In this study, we propose a minimal predator-prey coevolutionframework based on mixed cooperative-competitive multiagent reinforcementlearning, and adopt a reward function that is solely based on the fundamentalsurvival pressure, that is, prey receive a reward of $-1$ if caught bypredators while predators receive a reward of $+1$. Surprisingly, our analysisof this approach reveals an unexpectedly rich diversity of emergent behaviorsfor both prey and predators, including flocking and swirling behaviors forprey, as well as dispersion tactics, confusion, and marginal predationphenomena for predators. Overall, our study provides novel insights into thecollective behavior of organisms and highlights the potential applications inswarm robotics.

Predator-prey survival pressure is sufficient to evolve swarming behaviors