Application of Deep Reinforcement Learning Control of an Inverted Hydraulic Pendulum

Abstract

Deep reinforcement learning (RL) control is an emerging branch of machine learning focusing on data-driven solutions to complex nonlinear optimal control problems by trial-and-error learning. This study aims to apply deep reinforcement learning control to a hydromechanical system. The investigated system is an inverted pendulum on a cart with a hydraulic drive. The focus lies on implementing a comprehensive framework for the deep RL controller, which allows for training a control strategy in simulation and solving the tasks of swinging the pendulum up and balancing it. The RL controller can solve these challenges successfully; therefore, reinforcement learning presents a possibility for novel data-driven control approaches for hydromechanical systems.