Controlling Fish Schools via Reinforcement Learning of Virtual Fish Movement

ArXi:2603.16384v1 Announce Type: cross This study investigates a method to guide and control fish schools using virtual fish trained with reinforcement learning. We utilize 2D virtual fish displayed on a screen to overcome technical challenges such as durability and movement constraints inherent in physical robotic agents. To address the lack of detailed behavioral models for real fish, we adopt a model-free reinforcement learning approach.