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.

翻译：本研究探讨了一种利用强化学习训练的虚拟鱼来引导和控制鱼群的方法。我们采用屏幕上显示的二维虚拟鱼，以克服物理机器人代理固有的耐久性和运动限制等技术挑战。针对真实鱼类缺乏详细行为模型的问题，我们采用了无模型强化学习方法。首先，仿真结果表明，即使模拟的真实鱼类频繁忽略虚拟刺激，强化学习仍能习得有效的运动策略。其次，活鱼的真实世界实验证实，习得的策略能成功引导鱼群朝向指定的目标方向。统计分析表明，所提出的方法在包括无刺激状态和启发式"边缘停留"策略在内的基线条件下均表现出显著优势。本研究为如何通过人工代理利用强化学习影响动物集体行为提供了早期实证。