sinergym 3.0.6

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

Sinergym

Welcome to Sinergym!

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

For more information about Sinergym, we recommend to visit our documentation here.

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. Don't forget to take a look at CONTRIBUTING.md if you're thinking about contributing to Sinergym.

The main functionalities of Sinergym are the following :

• Compatibility with simulation engines. Communication between Python and EnergyPlus is established using Energyplus Python API as a middleware. However, more of them (e.g. OpenModelica) could be included in the backend while maintaining the Gymnasium API in the future.

• 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 which allows modifying experimental settings in an easy manner. The user can create his own environments, combining his own building model, weather, reward, observation/action space, variables, actuators, 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. Many of the updates to the environment definition require changes to the building model (epJSON file) to adapt it to these new features before the simulation starts, which Sinergym will perform automatically. For example, using another weather file requires building location and design days update, using new observation variables requires to update the Output:Variable and Output:Meter fields, the same occurs with extra configuration context concerned with simulation directly, if weather variability is set, then a weather with noise will be used. These new building and weather file versions, is saved in the Sinergym output folder, leaving the original intact. 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 actuators control. Related to the above, it will only be necessary to specify the name of the actuators to be controlled through the actions of the Gymnasium interface, and Sinergym will take care of everything.

• Extensive environment information. It is important that users can get some information about Sinergym background components from environment interface easily. From environment instance, it is possible to consult available schedulers, variables which compose an observation and action, whether simulator is running, the building run period, episode length, timesteps per episode, available building zones... And much more.

• 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 and logger specific information about Sinergym environments. However, Sinergym is completely agnostic to any DRL algorithm and can be used with any DRL library that works with gymnasium interface.

• 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 to do it.

• Weights & Biases tracking and visualization compatibility. One of Sinergym's objectives is to automate and facilitate the training, reproducibility and comparison of agents in simulation-based building control problems, managing and monitoring model lifecycle from training to deployment. WandB is an open-source platform for the machine learning lifecycle helping us with this issue. It lets us register experiments hyperparameters, visualize data recorded in real-time, and store artifacts with experiment outputs and best obtained models.

• 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 project in active development. Stay tuned for upcoming releases.

Project structure

This repository is organized in the next directories:

• sinergym/: Here you will find all the source code that forms Sinergym: environment, modeling, simulator, and tools such as wrappers, reward functions, etc.
• docs/: Online documentation, generated with Sphinx and using Restructured Text (RST).
• examples/: Here there are a set of notebooks written in Jupyter format. Use cases are illustrated with Sinergym.
• tests/: All the unit tests of Sinergym. Fixtures and checks are defined here in order to check that the tool is in a stable state.
• scripts/: This folder contains scripts in which Sinergym is used. We can find useful things for the users, such as agent training, agent loading, performance checks, etc. In addition, these scripts allow a high margin of configuration using the JSON format.

List of available environments

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

Installation

Please, visit INSTALL.md for more information about Sinergym installation.

Usage example

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 Gymnasium 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, info = env.reset()
terminated = False
R = 0.0
while not terminated:
    a = env.action_space.sample() # random action selection
    obs, reward, terminated, truncated, 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.

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

Google Cloud Platform support

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

Projects using Sinergym

The following are some of the projects benefiting from the advantages of Sinergym:

• Demosthen/ActiveRL
• VectorInstitute/HV-Ai-C
• rdnfn/beobench

:pencil: If you want to appear in this list, do not hesitate to send us a PR and include the following badge in your repository:

Citing Sinergym

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},
}