Sinergym: A Building Simulation and Control Framework for Training Reinforcement Learning Agents
Procs. 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (2nd ACM SIGEnergy Workshop on Reinforcement Learning for Energy Management in Buildings & Cities (RLEM)
, pp. 319–323
(2021)
Abstract:
We introduce Sinergym, an open-source building simulation and control framework for training reinforcement learning agents. The proposed framework is compatible with EnergyPlus models and allows to implement Python-based controllers, facilitating reproducibility of experiments and generalization to multiple scenarios. A comparison between Sinergym and other existing libraries for building control is included. We describe its design and main functionalities, such as offering a diverse set of environments with different buildings, weather types and action spaces. The provided examples show the usage of the framework for benchmarking reinforcement learning methods for building control.
Links:
| DOI: 10.1145/3486611.3488729 PDF: |
Bibtex:
@inproceedings{rlem2021,
title = {Sinergym: A Building Simulation and Control Framework for Training Reinforcement Learning Agents},
author = {Jiménez-Raboso, Javier and Campoy-Nieves, Alejandro and Manjavacas-Lucas, Antonio and Gómez-Romero, Juan and Molina-Solana, Miguel},
booktitle = {Procs. 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (2nd ACM SIGEnergy Workshop on Reinforcement Learning for Energy Management in Buildings \& Cities (RLEM)},
pages = {319--323},
numpages = {5},
year = {2021},
month = nov,
location = {Coimbra, Portugal},
series = {BuildSys '21},
timestamp = {134},
doi = {10.1145/3486611.3488729},
url = {https://rlem-workshop.net/},
isbn = {9781450391146},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA}
}