# ModelicaGym: Applying Reinforcement Learning to Modelica Models

arXiv: https://arxiv.org/abs/1909.08604 · HTML: https://mlsanity.com/paper/1909.08604/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 6 of 6)

- [Model-predictive control and reinforcement learning in multi-energy system case studies](https://mlsanity.com/paper/2104.09785/) (2021)
- [Safe reinforcement learning for multi-energy management systems with known constraint functions](https://mlsanity.com/paper/2207.03830/) (2022)
- [PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability](https://mlsanity.com/paper/2310.03906/) (2023)
- [SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning](https://mlsanity.com/paper/2412.08293/) (2024)
- [Reinforcement Learning for Sustainable Energy: A Survey](https://mlsanity.com/paper/2407.18597/) (2024)
- [A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration](https://mlsanity.com/paper/2404.12498/) (2024)

## Abstract (arXiv)

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.