ModelicaGym：将强化学习应用于Modelica模型
ModelicaGym: Applying Reinforcement Learning to Modelica Models
摘要：
本文介绍了ModelicaGym工具箱，该工具箱是为采用强化学习（RL）解决Modelica模型中的优化和控制任务而开发的。开发的工具允许使用Functional Mock-up Interface（FMI）将模型连接到OpenAI Gym工具包，以便利用Modelica基于方程的建模和协同仿真以及RL算法作为相应工具的功能。因此，ModelicaGym有利于快速方便地开发RL算法和它们的比较时，解决最优控制问题的Modelicadaptric模型。详细讨论了ModelicaGymtoolbox类的继承结构和实现方法。对Cart-Pole平衡问题进行了工具箱功能验证。包括物理系统模型的描述及其工具箱的集成，模型参数（力大小、车杆质量比、奖励比和仿真时间步长）的选择及其对Q-学习算法学习过程的影响的实验，并对仿真结果进行了讨论。
英文摘要：
This paper presents ModelicaGym toolbox that was developed to employ Reinforcement Learning (RL) for solving optimization and control tasks in Modelica models. The developed tool allows connecting models using Functional Mock-up Interface (FMI) toOpenAI Gym toolkit in order to exploit Modelica equation-based modelling and co-simulation together with RL algorithms as a functionality of the tools correspondingly. Thus, ModelicaGym facilitates fast and convenient development of RL algorithms and their comparison when solving optimal control problem for Modelicadynamic models. Inheritance structure ofModelicaGymtoolbox's classes and the implemented methods are discussed in details. The toolbox functionality validation is performed on Cart-Pole balancing problem. This includes physical system model description and its integration using the toolbox, experiments on selection and influence of the model parameters (i.e. force magnitude, Cart-pole mass ratio, reward ratio, and simulation time step) on the learning process of Q-learning algorithm supported with the discussion of the simulation results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
