用强化学习训练虚拟鱼，引导真实鱼群朝指定方向移动。
Controlling Fish Schools via Reinforcement Learning of Virtual Fish Movement
- 用2D虚拟鱼在屏幕上模拟，避免物理机器人限制
- 即使真实鱼常忽略刺激，仍能学会有效引导策略
- 实验证明效果优于无刺激和边缘停留策略
本研究探索通过强化学习训练虚拟鱼来引导和控制鱼群的方法。采用2D虚拟鱼显示在屏幕上，以克服物理机器人存在的耐久性与运动约束问题。针对真实鱼行为模型不完善的问题，采用无模型强化学习方法。仿真结果显示，即使模拟的真实鱼频繁忽略虚拟刺激，强化学习仍能获得有效的运动策略。真实实验表明，所学策略成功引导鱼群向指定目标方向移动。统计分析显示，该方法显著优于基线条件，包括无刺激和启发式‘停留在边缘’策略。本研究首次展示了强化学习如何通过人工代理影响群体动物行为。
原文摘要 · Abstract (English)
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.
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