# Rule-based policy regularization for reinforcement learning-based building control 



## Description

An in-depth paragraph about your project and overview of use.

## Getting Started

### Dependencies

* Please refer to [Sinergym's Github pages](https://github.com/ugr-sail/sinergym) for detailed description about how to install Sinergym and its dependencies (EnergyPlus and BCVBT)

### Installing

* After installing gym, Sinergym and other dependent libraries in our codes, you should be ready to run the experiments

* We sampled a small portion of the buffers to avoid exceeding the maximum size restriction of supplementary material, we will open-source the entire sets of buffers afterwards.

### Executing program

* Run the ```Sinergym.py``` for online approach or ```Sinergym_BRL.py``` for offline approach
* Here is an example:

```
python -u Sinergym_BRL.py --seed 2 --env Eplus-5Zone-mixed-continuous-stochastic-v1 --policy TD3_BC --buffer_folder <$where_your_buffers_locate>
```
