This repository is the official implementation of Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation.
To install requirements:
pip install -r requirements.txt
Please follow https://github.com/IBM/rl-testbed-for-energyplus and install EnergyPlus version 9.1.0
To run the PID agent, run
python train_pid.py --city SF
To train the PPO agent, run
python train_ppo.py --city SF
To train the model-based RL with random shooting (RS), run
python train_model_based.py --city SF --mpc_horizon 5 --num_days_on_policy 10 --training_epochs 100
To train the model-based RL with dagger, run
python train_model_based.py --city SF --mpc_horizon 5 --num_days_on_policy 10 --training_epochs 100 --dagger
It will create a folder called runs that includes all the state, action and rewards during the training.
The EnergyPlus generated files will be in the log folder.
- SF
- Golden
- Chicago
- Sterling
We also provide shell script file in case you want to run everything. Checkout
- run_pid.sh
- run_ppo.sh
- run_model_based_plan.sh
- run_model_based_dagger.sh
@article{Zhang2019BuildingHS,
title={Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation},
author={Chi Zhang and S. Kuppannagari and R. Kannan and V. Prasanna},
journal={Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
year={2019}
}Please also cite the paper that introduces the environment
@InProceedings{10.1007/978-981-13-2853-4_4,
author="Moriyama, Takao and De Magistris, Giovanni and Tatsubori, Michiaki and Pham, Tu-Hoa and Munawar, Asim and Tachibana, Ryuki",
title="Reinforcement Learning Testbed for Power-Consumption Optimization",
booktitle="Methods and Applications for Modeling and Simulation of Complex Systems",
year="2018",
publisher="Springer Singapore",
address="Singapore",
pages="45--59",
isbn="978-981-13-2853-4"
}