用深度强化学习让水母状机器人追移动目标，自适应流体干扰。
Deep reinforcement learning for tracking a moving target in jellyfish-like swimming
- 用DQN根据身体状态输出受力动作，控制柔性水母机器人。
- 引入动作调节，克服流体-结构交互导致的响应延迟。
- 适合研究柔性体在流体中智能控制的学者参考。
我们提出一种深度强化学习方法，训练水母状柔性机器人在二维流场中有效追踪移动目标。该机器人基于扭转弹簧的肌肉模型，采用深度Q网络（DQN）作为智能体，输入为机器人的几何与动态参数，输出为施加于机器人的作用力。为缓解复杂流体-结构相互作用带来的干扰，我们引入动作调节机制。目标是使机器人以最短时间抵达目标点。训练数据来自使用浸入边界法模拟的运动轨迹。追踪移动目标时，由于涡旋脱落与自身运动之间的水动力相互作用，存在力施加与身体响应之间的固有延迟。实验表明，结合DQN智能体和动作调节，机器人能根据瞬时状态动态调整航向。本工作拓展了机器学习在流体环境中控制柔性物体的应用范围。
原文摘要 · Abstract (English)
We develop a deep reinforcement learning method for training a jellyfish-like swimmer to effectively track a moving target in a two-dimensional flow. This swimmer is a flexible object equipped with a muscle model based on torsional springs. We employ a deep Q-network (DQN) that takes the swimmer's geometry and dynamic parameters as inputs, and outputs actions which are the forces applied to the swimmer. In particular, we introduce an action regulation to mitigate the interference from complex fluid-structure interactions. The goal of these actions is to navigate the swimmer to a target point in the shortest possible time. In the DQN training, the data on the swimmer's motions are obtained from simulations conducted using the immersed boundary method. During tracking a moving target, there is an inherent delay between the application of forces and the corresponding response of the swimmer's body due to hydrodynamic interactions between the shedding vortices and the swimmer's own locomotion. Our tests demonstrate that the swimmer, with the DQN agent and action regulation, is able to dynamically adjust its course based on its instantaneous state. This work extends the application scope of machine learning in controlling flexible objects within fluid environments.
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