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.

翻译：本文展示了人工智能（AI）通过强化学习（RL）学习控制Acrobot的计算与实验结果。实验装置被设计为嵌入式系统，这对机器人学和能量收集应用具有重要意义。具体而言，我们研究了Acrobot角速度的控制及其总能量（动能与势能之和）的控制。为此，强化学习算法被设计用于驱动Acrobot第一摆的角速度或能量趋近于目标值。通过该方法，实现了Acrobot非驱动摆的平动或完整旋转。此外，对Acrobot控制的研究揭示了状态空间离散化、回合长度、动作空间或驱动摆质量对强化学习控制的影响。通过大量的仿真与实验，评估了参数变化的效果。