Development of swarm behavior in artificial learning agents that adapt to different foraging environments
This work addresses the challenge of modeling adaptive collective behavior in AI agents for foraging scenarios, but it is incremental as it builds on existing reinforcement learning and swarm intelligence methods.
The study tackled the problem of how artificial learning agents develop swarm behavior in foraging environments by applying Projective Simulation within a reinforcement learning framework, resulting in the emergence of different collective motions (e.g., strongly aligned swarms for distant resources) and individual trajectory types (Lévy-like for distant, Brownian-like for nearby resources).
Collective behavior, and swarm formation in particular, has been studied from several perspectives within a large variety of fields, ranging from biology to physics. In this work, we apply Projective Simulation to model each individual as an artificial learning agent that interacts with its neighbors and surroundings in order to make decisions and learn from them. Within a reinforcement learning framework, we discuss one-dimensional learning scenarios where agents need to get to food resources to be rewarded. We observe how different types of collective motion emerge depending on the distance the agents need to travel to reach the resources. For instance, strongly aligned swarms emerge when the food source is placed far away from the region where agents are situated initially. In addition, we study the properties of the individual trajectories that occur within the different types of emergent collective dynamics. Agents trained to find distant resources exhibit individual trajectories with Lévy-like characteristics as a consequence of the collective motion, whereas agents trained to reach nearby resources present Brownian-like trajectories.