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Sinergym: A Building Simulation and Control Framework for Training Reinforcement Learning Agents

Jiménez-Raboso, Javier and Campoy-Nieves, Alejandro and Manjavacas-Lucas, Antonio and Gómez-Romero, Juan and Molina-Solana, Miguel
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}
}