Package: sinergym
SinergymWelcome to Sinergym!The goal of this project is to create an environment followingGymnasium interface, for wrapping simulation engines for building control usingdeep reinforcement learning.For more information about Sinergym, we recommend to visit ourdocumentation here.Please, help us to improve byreporting your questions and issueshere. It is easy, just 2 clicks using our issue templates (questions, bugs, improvements, etc.). More detailed info on how to report issueshere. Don't forget to take a look atCONTRIBUTING.mdif you're thinking about contributing to Sinergym.The main functionalities ofSinergymare the following :Compatibility with simulation engines. Communication between
Python andEnergyPlusis established
usingEnergyplus Python APIas 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 toAtariorMujocoenvironments
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 inSinergymand 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 asextra configurationas well, such as people occupant, time-steps per simulation hour, run-period, etc.Customizable components.Sinergymis 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 (epJSONfile) to adapt it to these new features before the simulation starts, whichSinergymwill
perform automatically. For example, using another weather file requires building location and design days update, using new
observation variables requires to update theOutput:VariableandOutput:Meterfields, 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 theSinergymoutput folder, leaving the original
intact. In short,Sinergymautomates 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, andSinergymwill take care of everything.Extensive environment information. It is important that users can get some information aboutSinergymbackground 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 aboutSinergymenvironments.
However,Sinergymis 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 withSinergym, we tell you some details about how to do it.Weights & Biases tracking and visualization compatibility. One ofSinergym'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.WandBis 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.Sinergymdevelops 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 extensivedocumentation,unit testsandgithub actions workflowsto makeSinergyman efficient ecosystem for both understanding and development.Many more!This is a project in active development. Stay tuned for upcoming releases.Project structureThis repository is organized in the next directories:sinergym/: Here you will find all the source code that formsSinergym: 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 environmentsIf you would like to see a complete and updated list of our available environments, please visitour listin the officialSinergymdocumentation.InstallationPlease, visitINSTALL.mdfor more information about Sinergym installation.Usage exampleIf you used our Dockerfile during installation, you should have thetry_env.pyfile 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 andSinergymrunning correctly.Sinergymuses the standard Gymnasium API. So basic loop should be something like:importgymnasiumasgymimportsinergym# Create the environmentenv=gym.make('Eplus-datacenter-mixed-continuous-stochastic-v1')# Initialize the episodeobs,info=env.reset()truncated=terminated=FalseR=0.0whilenot(terminatedortruncated):a=env.action_space.sample()# random action selectionobs,reward,terminated,truncated,info=env.step(a)# get new observation and rewardR+=rewardprint('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 ourusage examplesdocumentation section.Google Cloud Platform supportFor more information about this functionality, please, visit our documentationhere.Projects using SinergymThe following are some of the projects benefiting from the advantages of Sinergym:Demosthen/ActiveRLVectorInstitute/HV-Ai-Crdnfn/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:Repo ActivityCiting SinergymIf you useSinergymin your work, please cite ourpaper:@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},}