⚠️ Stable Baselines 3 are working in order to have gymnasium support. It is possible that SB3 algorithms don't work correctly with Sinergym environments temporally.

Welcome to Sinergym!

This is a project based on Zhiang Zhang and Khee Poh Lam Gym-Eplus.

The goal of this project is to create an environment following OpenAI Gym interface for wrapping simulation engines for building control using deep reinforcement learning.

Please, help us to improve by reporting your questions and issues here. It is easy, just 2 clicks using our issue templates (questions, bugs, improvements, etc.). More detailed info on how to report issues here.

The main functionalities of Sinergym are the following :

• Include different simulation engines. Communication between Python and EnergyPlus is established using BCVTB middleware. Since this tool allows for interacting with several simulation engines, more of them (e.g. OpenModelica) could be included in the backend while maintaining the Gym API.

• Benchmark environments. Similarly to Atari or Mujoco environments for RL community, we are designing a set of environments for benchmarking and testing deep RL algorithms. These environments may include different buildings, weathers, action/observation spaces, function rewards, etc.

• Customizable environments. We aim to provide a package that allows to modify experimental settings in an easy manner. The user can create his own environments defining his own building model, weather, reward, observation/action space and variables, environment name, etc. The user can also use these pre-configured environments available in Sinergym and change some aspect of it (for example, the weather) in such a way that he does not have to make an entire definition of the environment and can start from one pre-designed by us. Some parameters directly associated with the simulator can be set as extra configuration as well, such as people occupant, time-steps per simulation hour, run-period, etc.

• Customizable components: Sinergym is easily scalable by third parties. Following the structure of the implemented classes, new custom components can be created for new environments such as function rewards, wrappers, controllers, etc.

• Automatic Building Model adaptation to user changes: Building models (IDF) will be adapted to specification of each simulation by the user. For example, Designdays and Location components from IDF files will be adapted to weather file (EPW) specified in Sinergym simulator backend without any intervention by the user (only the environment definition). BCVTB middleware external interface in IDF model and variables.cfg file is generated when simulation starts by Sinergym, this definition depends on action and observation space and variables defined. In short, Sinergym automates the whole process of model adaptation so that the user only has to define what he wants for his environment.

• Automatic external interface integration for actions. Sinergym provides functionality to obtain information about the environments such as the zones or the schedulers available in the environment model. Using that information, which is possible to export in a excel, users can know which controllers are available in the building and, then, control them with an external interface from an agent. To do this, users will make an action definition in which it is indicated which default controllers they want to replace in a specific format and Sinergym will take care of the relevant internal changes in the model.

• Stable Baseline 3 Integration. Some functionalities like callbacks have been customized by our team in order to test easily these environments with deep reinforcement learning algorithms. This tool can be used with any other DRL library that supports the OpenAI gym interface as well.

• Google Cloud Integration. Whether you have a Google Cloud account and you want to use your infrastructure with Sinergym, we tell you some details about how doing it.

• Mlflow tracking server. Mlflow is an open source platform for the machine learning lifecycle. This can be used with Google Cloud remote server (if you have Google Cloud account) or using local store. This will help you to manage and store your runs and artifacts generated in an orderly manner.

• Data Visualization. Using Sinergym logger or Tensorboard server to visualize training and evaluation information in real-time.

• Notebooks examples. Sinergym develops code in notebook format with the purpose of offering use cases to the users in order to help them become familiar with the tool. They are constantly updated, along with the updates and improvements of the tool itself.

• This project is accompanied by extensive documentation, unit tests and github actions workflows to make Sinergym an efficient ecosystem for both understanding and development.

• Many more!

This is a work in progress project. Stay tuned for upcoming releases.

If you would like to see a complete and updated list of our available environments, please visit our list in the official Sinergym documentation.

For more detailed information, please visit our documentation.

We include a Dockerfile for installing all dependencies and setting up the image for running Sinergym.

By default, Dockerfile will do pip install -e .[extras], if you want to install a different setup, you will have to do in root repository:

  $ docker build -t <tag_name> --build-arg SINERGYM_EXTRAS=[<setup_tag(s)>] .

For example, if you want a container with only documentation libraries and testing:

  $ docker build -t example1/sinergym:latest --build-arg SINERGYM_EXTRAS=[doc,test] .

On the other hand, if you don't want any extra library, it's necessary to write an empty value like this:

  $ docker build -t example1/sinergym:latest --build-arg SINERGYM_EXTRAS= .

📝 You can install directly our container from Docker Hub repository <https://hub.docker.com/repository/docker/sailugr/sinergym>__, all releases of this project are there.

📝 If you use Visual Studio Code, by simply opening the root directory and clicking on the pop-up button Reopen in container, all the dependencies will be installed automatically and you will be able to run Sinergym in an isolated environment. For more information about how to use this functionality, check VSCode Containers extension documentation.

To install Sinergym manually instead of through the container (recommended), follow these steps:

• First, clone this repository:

  $ git clone https://github.com/ugr-sail/sinergym.git
  $ cd sinergym

• Then, it is recommended to create a virtual environment. You can do so by:

  $ sudo apt-get install python-virtualenv virtualenv
  $ virtualenv env_sinergym --python=python3.10
  $ source env_sinergym/bin/activate
  $ pip install -e .[extras]

• There are other alternatives like conda environments (recommended). Conda is very comfortable to use and we have a file to configure it automatically:

  $ cd sinergym
  $ conda env create -f python_environment.yml
  $ conda activate sinergym

• Now, we have a correct python version with required modules to run Sinergym. Let's continue with the rest of the programs that are needed outside of Python to run the simulations:

Install EnergyPlus. Currently it has been update compatibility to 9.5.0 and it has been tested, but code may also work with other versions. Sinergym ensure this support:

Other combination may works, but they don't have been tested.

Follow the instructions here and install it for Linux (only Ubuntu is supported). Choose any location to install the software. Once installed, a folder called Energyplus-9-5-0 should appear in the selected location.

Follow the instructions here for installing BCVTB software. Another option is to copy the bcvtb folder from this repository

Two environment variables must be set: EPLUS_PATH and BCVTB_PATH, with the locations where EnergyPlus and BCVTB are installed respectively.

As we have told you in section Manual Installation, Python environment can be set up using python_environment.yml with conda. However, we can make an installation using the Github repository itself:

  $ cd sinergym
  $ pip install -e .

Extra libraries can be installed by typing pip install -e .[extras]. extras include all optional libraries which have been considered in this project such as testing, visualization, Deep Reinforcement Learning, monitoring , etc. It's possible to select a subset of these libraries instead of 'extras' tag in which we select all optional libraries, for example:

  $ pip install -e .[test,doc]

In order to check all our tag list, visit setup.py <https://github.com/ugr-sail/sinergym/blob/main/setup.py>__ in Sinergym root repository. In any case, they are not a requirement of the package.

You can also install from oficial pypi repository <https://pypi.org/project/sinergym/>__ with last stable version by default:

  $ pip install sinergym[extras]

This project is automatically supervised using tests developed specifically for it. If you want to check Sinergym has been installed successfully, run next command:

$ pytest tests/ -vv

Anyway, every time Sinergym repository is updated, the tests will run automatically in a remote container using the Dockerfile to build it. Github Action <https://docs.github.com/es/actions/>__ will do that job (see our documentation for more information).

If you used our Dockerfile during installation, you should have the try_env.py file in your workspace as soon as you enter in. In case you have installed everything on your local machine directly, place it inside our cloned repository. In any case, we start from the point that you have at your disposal a terminal with the appropriate python version and Sinergym running correctly.

Sinergym uses the standard OpenAI gym API. So basic loop should be something like:

import gymnasium as gym
import sinergym
# Create the environment
env = gym.make('Eplus-datacenter-mixed-continuous-stochastic-v1')
# Initialize the episode
obs = env.reset()
done = False
R = 0.0
while not done:
    a = env.action_space.sample() # random action selection
    obs, reward, done, info = env.step(a) # get new observation and reward
    R += reward
print('Total reward for the episode: %.4f' % R)
env.close()

Notice that a folder will be created in the working directory after creating the environment. It will contain the EnergyPlus outputs produced during the simulation.

📝 For more examples and details, please visit our usage examples documentation section.

Cloud Computing

For more information about this functionality, please, visit our documentation here.

If you use Sinergym in your work, please cite our paper:

@inproceedings{2021sinergym,
    title={Sinergym: A Building Simulation and Control Framework for Training Reinforcement Learning Agents}, 
    author={Jiménez-Raboso, Javier and Campoy-Nieves, Alejandro and Manjavacas-Lucas, Antonio and Gómez-Romero, Juan and Molina-Solana, Miguel},
    year={2021},
    isbn = {9781450391146},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3486611.3488729},
    doi = {10.1145/3486611.3488729},
    booktitle = {Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
    pages = {319–323},
    numpages = {5},
}