Multi-Agent Reinforcement Learning for HVAC Control in Buildings
# Clone the repository
git clone https://github.com/BuildingGym/hvacmarl6e43
cd hvacmarl6e43
# Install the package and dependencies
python -m pip install -e .
python -m pip install --extra-index-url https://test.pypi.org/simple git+https://github.com/NTU-CCA-HVAC-OPTIM-a842a748/EnergyPlus-OOEP
pip install -r requirements.txthvacmarl6e43/
├── packages/
│ ├── hvacmarl6e43/ # Core source code and utilities
│ │ ├── utils.py # Utility functions
│ │ └── buildings/ # Building models (IDF files)
│ └── hvacmarl6e43_notebooks/ # Experiment notebooks
│ ├── run_baselines.ipynb # Run baseline experiments
│ ├── run_monoagent.ipynb # Run single-agent RL experiments
│ ├── run_multiagent.ipynb# Run multi-agent RL experiments
│ ├── viz_metrics.ipynb # Visualize experiment metrics
│ └── viz_obs.ipynb # Visualize observations
├── pyproject.toml
├── requirements.txt
└── README.md
Main experiment scripts are located in packages/hvacmarl6e43_notebooks/:
| Notebook | Description |
|---|---|
run_baselines.ipynb |
Run baseline experiments |
run_monoagent.ipynb |
Run single-agent reinforcement learning experiments |
run_multiagent.ipynb |
Run multi-agent reinforcement learning experiments |
viz_metrics.ipynb |
Visualize experiment metrics |
viz_obs.ipynb |
Visualize observation data |
# Open with Jupyter or VS Code to run experimentsIf you find this work useful, please cite our paper:
@article{guan2025adaptive,
title={Adaptive Multi-Agent HVAC Control for Thermal Comfort Using Multi-Agent PPO with Population-Based Training},
author={Guan, Songze and Chen, Ruotian and Liu, Jiuwei and Zhou, Kate Qi and Dai, Xilei and Li, Wentai and Somasundaram, Sivanand and Chong, Alex and Lee, Christopher HT and Yuen, Chau},
journal={Energy and Buildings},
pages={116882},
year={2025},
publisher={Elsevier}
}MIT License