Welcome to sinergym!

The goal of Sinergym is to create an environment following Gymnasium interface for wrapping simulation engines (EnergyPlus) for building control using deep reinforcement learning or any external control.

Note

Please, help us to improve by reporting your questions and issues here. It is easy, just 2 clicks using our issue templates (questions, bugs, improvements, etc.). More detailed info on how to report issues here.

Sinergym offers:

Simulation Engine Compatibility: Uses EnergyPlus Python API for Python-EnergyPlus communication. Future plans include more engines like OpenModelica <https://openmodelica.org/>.
Benchmark Environments: Designs environments for benchmarking and testing deep RL algorithms or other external strategies, similar to Atari or Mujoco.
Customizable Environments: Allows easy modification of experimental settings. Users can create their own environments or modify pre-configured ones in Sinergym.
Customizable Components: Enables creation of new custom components for new environments, making Sinergym scalable, such as function rewards, wrappers,controllers, etc.
Automatic Building Model Adaptation: Sinergym automates the process of adapting the building model to user changes in the environment definition.
Automatic Actuators Control: Controls actuators through the Gymnasium interface based on user specification, only actuators names are required and Sinergym will do the rest.
Extensive Environment Information: Provides comprehensive information about Sinergym background components from the environment interface.
Stable Baseline 3 Integration: Customizes functionalities for easy testing of environments with SB3 algorithms, such as callbacks and cutomizable training real-time logging. However, Sinergym is agnostic to any DRL algorithm.
Google Cloud Integration: Offers guidance on using Sinergym with Google Cloud infrastructure.
Weights & Biases Compatibility: Automates and facilitates training, reproducibility, and comparison of agents in simulation-based building control problems. WandB assists in managing and monitoring model lifecycle.
Notebook Examples: Provides code in notebook format for user familiarity with the tool.
Extensive Documentation, Unit Tests, and GitHub Actions Workflows: Ensures Sinergym is an efficient ecosystem for understanding and development.
And much more!

Important

If you want to introduce your own buildings in Sinergym, please visit Adding New Buildings for Environments section.

Note

This is a work in progress project. Stay tuned for upcoming releases!

Contributing

For all those interested in improving Sinergym, there are always improvements to be made. You can check issues in the repository.

If you want to contribute, please read CONTRIBUTING.md first.

Projects using Sinergym

The following are some of the projects benefiting from the advantages of Sinergym:

If you want to appear in this list, do not hesitate to send us a PR and include the following badge in your repository:

Examples

The examples can be run if you have your computer or container properly configured (see Installation section) from our notebooks hosted in the examples folder of the official Sinergym repository.

Citing Sinergym

If you use Sinergym in your work, please cite our paper:

@inproceedings{2021sinergym,
  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},
  year={2021},
  isbn = {9781450391146},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3486611.3488729},
  doi = {10.1145/3486611.3488729},
  booktitle = {Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
  pages = {319–323},
  numpages = {5},
}