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A framework to designe Reinforcement Learning (RL) environments for Active Flow Control (AFC), as well as a playground to train Deep Reinforcement Learning (DRL) algorithms for AFC applications.
Gym-preCICE is a Python preCICE adapter fully compliant with Gymnasium (also known as OpenAI Gym) API to facilitate designing and developing Reinforcement Learning (RL) environments for single- and multi-physics active flow control (AFC) applications. In an actor-environment setting, Gym-preCICE takes advantage of preCICE, an open-source coupling library for partitioned multi-physics simulations, to handle information exchange between a controller (actor) and an AFC simulation environment. The developed framework results in a seamless non-invasive integration of realistic physics-based simulation toolboxes with RL algorithms.
Gymnasium: Installed by default. Refer to the Gymnasium for more information.
preCICE: You need to install the preCICE library. Refer to the preCICE documentation for information on building and installation.
preCICE Python bindings: Installed by default. Refer to the python language bindings for preCICE for information.
We support and test for Python versions 3.7 and higher on Linux. We recommend installing Gym-preCICE within a virtual environment, e.g. conda:
conda create -n gymprecice python=3.8
conda activate gymprecice
python3 -m pip install gymprecice
gymprecice
installation (this should pass silently without any error/warning messages):python3 -c "import gymprecice"
The default installation does not include extra dependencies to run tests or tutorials. You can install these dependencies like python3 -m pip install gymprecice[test]
, or
python3 -m pip install gymprecice[tutorial]
, or use python3 -m pip install gymprecice[all]
to install all extra dependencies.
git clone https://github.com/gymprecice/gymprecice.git
cd gymprecice
pip install -e .
pip install -e .[dev]
pre-commit install
We use pytest
framework to run unit tests for all modules in our package. You need to install required dependencies before running any test:
python3 -m pip install gymprecice[test]
pytest ./tests
core.py
):pytest ./tests/test_core.py
Please refer to tutorials for the details on how to use the adapter. You can check out the Quickstart in our tutorials repository to try a ready-to-run control case. You need to install some of the required dependencies before running any tutorial:
python3 -m pip install gymprecice[tutorial]
If you use Gym-preCICE, please cite the following paper:
@misc{shams2023gymprecice,
title={Gym-preCICE: Reinforcement Learning Environments for Active Flow Control},
author={Mosayeb Shams and Ahmed H. Elsheikh},
year={2023},
eprint={2305.02033},
archivePrefix={arXiv}
}
See the contributing guidelines CONTRIBUTING.md for information on submitting issues and pull requests.
Gym-preCICE and its tutorials are primarily developed and maintained by:
This work was supported by the Engineering and Physical Sciences Research Council grant number EP/V048899/1.
Gym-preCICE and its tutorials are MIT licensed.
FAQs
A framework to designe Reinforcement Learning (RL) environments for Active Flow Control (AFC), as well as a playground to train Deep Reinforcement Learning (DRL) algorithms for AFC applications.
We found that gymprecice demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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