Original implementation of PEARL (Probabilsitic Emission Abating Reinforcement Learning), a building control algorithm proposed in the paper Low Emissions Building Control with Zero-Shot Reinforcement Learning by Scott R. Jeen, Alessandro Abate and Jonathan M. Cullen (2022).
PEARL combines ideas from system identification and model-based RL to find emission-reducing control policies zero-shot, that is, without access to a simulator or historical data a priori. In experiments across three varied building energy simulations, we show PEARL outperforms an existing RBC once, and popular RL baselines in all cases, reducing building emissions by as much as 31% whilst maintaining thermal comfort
First you'll need to install Energym for running the building energy simulations atop EnergyPlus. The full installation instructions can be found on their website here.
Then you can install the required dependencies via conda:
conda env create --file env.yaml
conda activate PEARL
Then, to recreate the main results from the paper run
python main.py
from the root directory.
If you found this work useful, or you use this project to inform your own research, please consider citing it with:
@article{jeen2022,
url = {https://arxiv.org/abs/2206.14191},
author = {Jeen, Scott R. and Abate, Alessandro and Cullen, Jonathan M.},
title = {Low Emission Building Control with Zero Shot Reinforcement Learning},
publisher = {arXiv},
year = {2022},
}
This work licensed under CC-BY-NC, see LICENSE.md for further details.