Building Control with RL using BOPTEST

Apply reinforcement learning to a building emulator to intelligently control HVAC systems.

Authors:

• Javier Arroyo
• David Blum
• Kyle Benne
• Iago Figueroa

Originally presented at Climate Change AI Summer School 2022.

Recorded talk here

Access this tutorial

We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.

We estimate that this tutorial will take around 10 minutes to execute from end-to-end.

Contribute to this tutorial

Please refer to these GitHub instructions to open a pull request via the "fork and pull request" workflow.

Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness.

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License

Usage of this tutorial is subject to the MIT License.

Cite

Plain Text

Arroyo, J., Blum, D., Benne, K., & Figueroa, I. (2024). Building Control with RL using BOPTEST [Tutorial]. In Climate Change AI Summer School 2024. Climate Change AI. https://doi.org/10.5281/zenodo.11553263

BibTeX

@misc{arroyo2024building,
  title={Building Control with RL using BOPTEST},
  author={Arroyo, Javier and Blum, David and Benne, Kyle and Figueroa, Iago},
  year={2024},
  organization={Climate Change AI},
  type={Tutorial},
  doi={https://doi.org/10.5281/zenodo.11553263},
  booktitle={Climate Change AI Summer School 2024},
  howpublished={\url{https://github.com/climatechange-ai-tutorials/building-control-boptest}}
}