WOFOST-Gym Documentation¶

WOFOST-Gym is a benchmark built using OpenAI’s Gymnasium and PCSE for crop yield agriculture simulations that spans annual and perennial crops. It natively supports training a variety of Reinforcement Learning Agents and generating data for other RL problems including Offline RL, Off Policy Evaluation, and Transfer Learning.

To visit the associated paper website, click here.

To view the associated paper, click this link.

Email Will Solow (soloww@oregonstate.edu) with bug reports or feature requests.

To cite this work, please cite the research paper as follows:

@article{solow_wofostgym_2025,
   title={WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies},
   author={William Solow and Sandhya Saisubramanian and Alan Fern},
   year={2025},
   eprint={2502.19308},
   archivePrefix={arXiv},
   primaryClass={cs.AI},
   url={https://arxiv.org/abs/2502.19308},
}