ModelicaGym: Applying Reinforcement Learning to Modelica Models

09/18/2019
by   Oleh Lukianykhin, et al.
0

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.

READ FULL TEXT
research
05/09/2020

Reinforcement Learning for Thermostatically Controlled Loads Control using Modelica and Python

The aim of the project is to investigate and assess opportunities for ap...
research
05/29/2023

RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion

This letter presents a versatile control method for dynamic and robust l...
research
10/09/2018

ns3-gym: Extending OpenAI Gym for Networking Research

OpenAI Gym is a toolkit for reinforcement learning (RL) research. It inc...
research
02/14/2022

QuadSim: A Quadcopter Rotational Dynamics Simulation Framework For Reinforcement Learning Algorithms

This study focuses on designing and developing a mathematically based qu...
research
03/04/2021

Neuromechanics-based Deep Reinforcement Learning of Neurostimulation Control in FES cycling

Functional Electrical Stimulation (FES) can restore motion to a paralyse...
research
01/20/2022

Transfer Learning for Operator Selection: A Reinforcement Learning Approach

In the past two decades, metaheuristic optimization algorithms (MOAs) ha...
research
07/07/2022

Robust optimal well control using an adaptive multi-grid reinforcement learning framework

Reinforcement learning (RL) is a promising tool to solve robust optimal ...

Please sign up or login with your details

Forgot password? Click here to reset