This paper applies reinforcement learning to train a predator to hunt multiple prey, which are able to reproduce, in a 2D simulation. It is shown that, using methods of curriculum learning, long-term reward discounting and stacked observations, a reinforcement-learning-based predator can achieve an economic strategy: Only hunt when there is still prey left to reproduce in order to maintain the population. Hence, purely selfish goals are sufficient to motivate a reinforcement learning agent for long-term planning and keeping a certain balance with its environment by not depleting its resources. While a comparably simple reinforcement learning algorithm achieves such behavior in the present scenario, providing a suitable amount of past and predictive information turns out to be crucial for the training success.

Flocking or swarm behavior is a widely observed phenomenon in nature. Although the entities might have self-interested goals like evading predators or foraging, they group themselves together because a collaborative observation is superior to the observation of a single individual. In this paper, we evaluate the emergence of swarms in a foraging task using multi-agent reinforcement learning (MARL). Every individual can move freely in a continuous space with the objective to follow a moving target object in a partially observable environment. The individuals are self-interested as there is no explicit incentive to collaborate with each other. However, our evaluation shows that these individuals learn to form swarms out of self-interest and learn to orient themselves to each other in order to find the target object even when it is out of sight for most individuals.

In nature, flocking or swarm behavior is observed in many species as it has beneficial properties like reducing the probability of being caught by a predator. In this paper, we propose SELFish (Swarm Emergent Learning Fish), an approach with multiple autonomous agents which can freely move in a continuous space with the objective to avoid being caught by a present predator. The predator has the property that it might get distracted by multiple possible preys in its vicinity. We show that this property in interaction with self-interested agents which are trained with reinforcement learning solely to survive as long as possible leads to flocking behavior similar to Boids, a common simulation for flocking behavior. Furthermore we present interesting insights into the swarming behavior and into the process of agents being caught in our modeled environment.