WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies
This addresses a gap for researchers in agricultural AI by providing a tool to explore RL applications in crop management, though it is incremental as it builds on existing simulation concepts.
The authors tackled the lack of crop simulators for training reinforcement learning agents in agriculture, particularly for perennial crops and multi-farm settings, by introducing WOFOSTGym, which supports 23 annual and two perennial crops and enables learning of diverse management strategies.
We introduce WOFOSTGym, a novel crop simulation environment designed to train reinforcement learning (RL) agents to optimize agromanagement decisions for annual and perennial crops in single and multi-farm settings. Effective crop management requires optimizing yield and economic returns while minimizing environmental impact, a complex sequential decision-making problem well suited for RL. However, the lack of simulators for perennial crops in multi-farm contexts has hindered RL applications in this domain. Existing crop simulators also do not support multiple annual crops. WOFOSTGym addresses these gaps by supporting 23 annual crops and two perennial crops, enabling RL agents to learn diverse agromanagement strategies in multi-year, multi-crop, and multi-farm settings. Our simulator offers a suite of challenging tasks for learning under partial observability, non-Markovian dynamics, and delayed feedback. WOFOSTGym's standard RL interface allows researchers without agricultural expertise to explore a wide range of agromanagement problems. Our experiments demonstrate the learned behaviors across various crop varieties and soil types, highlighting WOFOSTGym's potential for advancing RL-driven decision support in agriculture.