Controlling Fish Schools via Reinforcement Learning of Virtual Fish Movement
This work provides an early demonstration of using reinforcement learning to influence collective animal behavior, which could benefit fields like ecology or robotics, though it is incremental in applying existing methods to a new domain.
This study tackled the problem of guiding fish schools by using virtual fish trained with reinforcement learning, and it showed that the learned policy successfully directed fish toward target directions in real-world experiments, significantly outperforming baseline conditions.
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. First, simulation results show that reinforcement learning can acquire effective movement policies even when simulated real fish frequently ignore the virtual stimulus. Second, real-world experiments with live fish confirm that the learned policy successfully guides fish schools toward specified target directions. Statistical analysis reveals that the proposed method significantly outperforms baseline conditions, including the absence of stimulus and a heuristic "stay-at-edge" strategy. This study provides an early demonstration of how reinforcement learning can be used to influence collective animal behavior through artificial agents.