- Python 3.9
- The optimization toolkit framework is built on OpenAI Gymnasium
├── env.yaml : Conda setup file with package requirements
├── setup.py : Python setup file with requirements files
├── README.md : Readme documentation
├── utests : Folder containing a collection of unit tests
├── jlab_opt_control
├── agents : Folder containing different agents
├── buffers : Folder containing different buffers
├── cfgs : Folder containing configuration filesfor agents and environments
├── core : Folder containing base classes
├── drivers : Folder containing workflow modules / drivers
├── envs : Folder containing different environemments
├── models : Folder containing different models
├── utils : Folder containing supporting tools (e.g. monitoring)
- Clone code from repo and move into directory
git clone https://github.com/JeffersonLab/SciOptControlToolkit.git
cd SciOptControlToolkit
- Create default conda environment setup: (only once)
conda env create --file env.yaml
- Activate conda environment: (required every time you use the package)
conda activate jlab_opt_control_env
- Install the package in environment (only once)
pip install -e .
@misc{SOCT,
author = {Malachi Schram, Kishan Rajput, Armen Kasparian},
title = {Scientific Optimization Control Toolkit (SOCT)},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{JeffersonLab/SciOptControlToolkit}},
}
If you have any questions or concerns regarding SOCT, please contact Malachi Schram (schram@jlab.org), Kishan Rajput (kishan@jlab.org), Armen Kasparian (armenk@jlab.org).