CropGym: a Reinforcement Learning Environment for Crop Management
Nitrogen fertilizers have a detrimental effect on the environment, which can be reduced by optimizing fertilizer management strategies. We implement an OpenAI Gym environment where a reinforcement learning agent can learn fertilization management policies using process-based crop growth models and identify policies with reduced environmental impact. In our environment, an agent trained with the Proximal Policy Optimization algorithm is more successful at reducing environmental impacts than the other baseline agents we present.
H. Overweg, H. N. C. Berghuijs, I. N. Athanasiadis, CropGym: a Reinforcement Learning Environment for Crop Management, Computing Research Repository, AIMOCC 2021 Workshop at 9th Int'l Conf Learning Representations (ICLR 2021), 2021.
You might also enjoy (View all publications)
- The flows of nature to people, and of people to nature: applying movement concepts to ecosystem services
- Crop2ML: An open-source multi-language modeling framework for the exchange and reuse of crop model components
- CropGym: a Reinforcement Learning Environment for Crop Management