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https://github.com/vermouth1992/mbrl-hvac
Model-based Reinforcement Learning for Building HVAC Control
https://github.com/vermouth1992/mbrl-hvac
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Model-based Reinforcement Learning for Building HVAC Control
- Host: GitHub
- URL: https://github.com/vermouth1992/mbrl-hvac
- Owner: vermouth1992
- License: mit
- Created: 2019-05-08T20:19:24.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-09-19T11:22:57.000Z (over 1 year ago)
- Last Synced: 2023-09-19T13:40:22.797Z (over 1 year ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 8.38 MB
- Stars: 15
- Watchers: 1
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Model-based Reinforcement Learning for Building HVAC Control
This repository is the official implementation of [Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation](https://arxiv.org/abs/1910.05313).
## Requirements
To install requirements:
### Python package
```setup
pip install -r requirements.txt
```
### EnergyPlus
Please follow https://github.com/IBM/rl-testbed-for-energyplus and install EnergyPlus version 9.1.0
## Training
To run the PID agent, run
```train
python train_pid.py --city SF
```
To train the PPO agent, run
```train
python train_ppo.py --city SF
```
To train the model-based RL with random shooting (RS), run
```train
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
```train
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.
### Available cities
- 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
## Citation
```bib
@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
```bib
@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"
}
```