# SWATgym

**Repository Path**: mirrors_ibm/SWATgym

## Basic Information

- **Project Name**: SWATgym
- **Description**: SWATgym: a reinforcement learning environment for crop management.
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-10-24
- **Last Updated**: 2025-08-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SWATgym

SWATgym is a reinforcement learning environment based on the Soil and Water Assessment Tool ([SWAT](https://swat.tamu.edu/)). SWAT is a physics based, continuous time, semi-distributed river basin model that has been widely used to evaluate the effects of crop management decisions on water resources ([Arnold et al., 2012][arnold2012swat]). SWATgym demonstrates the application of reinforcement learning to crop management and enables one to evaluate various decision-making strategies on a full growing season.

Similar to the original SWAT model ([Arnold et al., 1998][arnold1998large]), SWATgym operates on a daily time step and considers various processes including:
- crop growth,
- hydrology, 
- nutrient cycles,
- weather,
- management inputs (fertilizer, irrigation).

## Getting Started
All dependencies are included in the [environment.yml](https://github.com/IBM/SWATgym/blob/main/environment.yml) file.

1. Install SWATgym from source by running
```
git clone https://github.com/IBM/SWATgym
```

2. After cloning, create a virtual environment e.g., using Conda: 
```
conda env create --name swat_env --file=environment.yml
```

3. Activate the environment: `conda activate swat_env`

4. Run an example with a Random policy agent: `python main.py`

## References

[Arnold et al., 1998. *Large area hydrologic modeling and assessment part I: model development*. Journal of the American Water Resources Association, 34 (1), 73–89.][arnold1998large]

[Arnold et al., 2012. *SWAT: model use, calibration, and validation*. Transactions of the ASABE, 55 (4), 1491–1508.][arnold2012swat]

## Citing this work

```
@article{madondo2023swat,
  title={A SWAT-based Reinforcement Learning Framework for Crop Management},
  author={Madondo, Malvern and Azmat, Muneeza and Dipietro, Kelsey and Horesh, Raya and Jacobs, Michael and Bawa, Arun and Srinivasan, Raghavan and O'Donncha, Fearghal},
  journal={arXiv preprint arXiv:2302.04988},
  year={2023}
}
```

[arnold1998large]: https://pubag.nal.usda.gov/download/75/pdf
[arnold2012swat]: https://swat.tamu.edu/media/99051/azdezasp.pdf