Virtual Commissioning Simulation as OpenAI Gym - A Reinforcement Learning Environment for Control Systems

Florian Jaensch, Lars Klingel, A. Verl
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引用次数: 1

Abstract

Manual development of control systems’ software is time-consuming and error-prone. Thus, high costs are already incurred in this phase of mechatronic system development. Virtual prototypes have so far only been used for testing purposes, such as virtual commissioning, but not for the automated creation of the control. A good test environment can also be extended to a learning environment with appropriate trial and error based algorithms. This work shows an approach to extend an industrial software tool for virtual commissioning as a standardized OpenAI gym environment. Thereby, established reinforcement learning algorithms can be used more easily and a step towards an industrial application of self-learning control systems can be made. The goal of this work is to provide industry and research with a platform for easy entry into the field of reinforcement learning.
基于OpenAI Gym的虚拟调试仿真——控制系统的强化学习环境
手动开发控制系统软件既费时又容易出错。因此,在机电一体化系统开发的这一阶段已经产生了很高的成本。到目前为止,虚拟原型仅用于测试目的,例如虚拟调试,而不是用于控件的自动创建。良好的测试环境也可以通过适当的基于试错算法扩展为学习环境。这项工作展示了一种将工业软件工具扩展为虚拟调试作为标准化OpenAI健身环境的方法。因此,可以更容易地使用已建立的强化学习算法,并且可以向自学习控制系统的工业应用迈出一步。这项工作的目标是为工业和研究提供一个容易进入强化学习领域的平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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