Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning

arXiv:2404.01999v1 Announce Type: cross
Abstract: Reinforcement learning (RL) is a flexible and efficient method for programming micro-robots in complex environments. Here we investigate whether reinforcement learning can provide insights into biological systems when trained to perform chemotaxis. Namely, whether we can learn about how intelligent agents process given information in order to swim towards a target. We run simulations covering a range of agent shapes, sizes, and swim speeds to determine if the physical constraints on biological swimmers, namely Brownian …

abstract agent agents arxiv cs.lg cs.ma emergence environments information insights intelligent learn micro multi-agent physics.bio-ph process programming reinforcement reinforcement learning robots strategies systems type