Predator–prey survival pressure is sufficient to evolve swarming behaviors

Abstract

The comprehension of how local interactions arise in global collective behavior is of utmost importance in both biological and physical research. Traditional agent-based models often rely on static rules that fail to capture the dynamic strategies of the biological world. Reinforcement learning has been proposed as a solution, but most previous methods adopt handcrafted reward functions that implicitly or explicitly encourage the emergence of swarming behaviors. In this study, we propose a minimal predator-prey coevolution framework based on mixed cooperative-competitive multiagent reinforcement learning, and adopt a reward function that is solely based on the fundamental survival pressure, that is, prey receive a reward of −1 if caught by predators while predators receive a reward of +1. Surprisingly, our analysis of this approach reveals an unexpectedly rich diversity of emergent behaviors for both prey and predators, including flocking and swirling behaviors for prey, as well as dispersion tactics, confusion, and marginal predation phenomena for predators. Overall, our study provides novel insights into the collective behavior of organisms and highlights the potential applications in swarm robotics.

Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics

“…As the first attempt of transferring the classical prey-predator interaction study from the virtual computer world to the real world, this study has provided possible techniques to implement complex scenarios with multi-robots interacts in a multi-modal way. Implementing prey-predator interaction in the real world with physical constrains intrinsically embedded benefits the studies in many ways as that of robotics-inspired biology [2,23,29,31,[38][39][40][41][42].…”

“…The recent development of agent-based simulation [22,23] and the robotics technology [24][25][26][27][28][29] provides an opportunity to devise a system that could conduct real-time and real-world experiments of prey-predator interaction scenarios with multi-modal sensory and evolutionary processing involved. We highlight that bringing the multi-modal preypredator interaction studies from virtual computer to real physical world matters at least in the following aspects: (1) Robots, designed to navigate complex environments, offer a more precise depiction of interactions than any other simulations [30][31][32][33].…”