CropGym: a Reinforcement Learning Environment for Crop Management

Research output: Working paperPreprint

Abstract

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.
Original languageEnglish
PublisherarXiv
Number of pages8
Publication statusPublished - May 2021

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