论文标题
基于强化学习的杂技控制的实验研究
Experimental Study on Reinforcement Learning-based Control of an Acrobot
论文作者
论文摘要
我们介绍了有关人工智能（AI）如何学习使用增强学习（RL）来控制杂技演员的计算和实验结果。因此，实验设置设计为嵌入式系统，这对于机器人和能量收集应用来说是感兴趣的。具体而言，我们研究了杂技的角度速度的控制，及其总能量的控制是动力学和势能的总和。通过这种方式，RL算法旨在驱动角速度或杂技演员的第一个摆的能量向所需值。这样，就可以实现杂技演员的未发动摆的文库或完全旋转。此外，对杂技控制进行了调查，从而实现了有关状态空间离散化，发作长度，动作空间或驱动的摆在RL控制的影响的见解。通过进一步的众多模拟和实验，评估了参数变化的影响。
We present computational and experimental results on how artificial intelligence (AI) learns to control an Acrobot using reinforcement learning (RL). Thereby the experimental setup is designed as an embedded system, which is of interest for robotics and energy harvesting applications. Specifically, we study the control of angular velocity of the Acrobot, as well as control of its total energy, which is the sum of the kinetic and the potential energy. By this means the RL algorithm is designed to drive the angular velocity or the energy of the first pendulum of the Acrobot towards a desired value. With this, libration or full rotation of the unactuated pendulum of the Acrobot is achieved. Moreover, investigations of the Acrobot control are carried out, which lead to insights about the influence of the state space discretization, the episode length, the action space or the mass of the driven pendulum on the RL control. By further numerous simulations and experiments the effects of parameter variations are evaluated.
