PEARL: Probabilistic Emission-Abating Reinforcement Learning

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).

[Paper] 

Method

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

Top: Cumulative emissions produced by all agents across the (a) Mixed Use, (b) Offices, and (c) Seminar Centre environments. Curves represent the mean of 3 runs of the experiment, shaded areas are one standard deviation (too small to see in all cases except PPO). Bottom: Mean daily building temperature produced by all agents, the green shaded area illustrates the target temperature range [19, 24].

Installation

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.

Citation

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},
}

License

This work licensed under CC-BY-NC, see LICENSE.md for further details.