用强化学习优化作物管理，支持多年生与一年生作物。
WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies
- 构建农业模拟环境，支持23种一年生和2种多年生作物。
- 在多农场、多年份场景中实现产量与环保平衡的决策优化。
- 适合无农业背景的研究者探索智能农事策略。
我们提出WOFOSTGym，一个新型作物模拟环境，用于训练强化学习（RL）代理在单农场和多农场设置下优化一年生和多年生作物的农事管理决策。有效的作物管理需在提升产量与经济效益的同时减少环境影响，这是一个复杂的序列决策问题，非常适合用强化学习解决。然而，现有研究缺乏适用于多年生作物的多农场模拟器，且多数作物模拟器不支持多种一年生作物。WOFOSTGym填补了这些空白，支持23种一年生作物和2种多年生作物，使强化学习代理能在多年、多作物、多农场环境下学习多样化的农事管理策略。该模拟器提供了一系列具有挑战性的任务，涵盖部分可观测性、非马尔可夫动态和延迟反馈等复杂特性。其标准强化学习接口使无农业背景的研究者也能探索广泛的农事管理问题。实验结果展示了不同作物品种和土壤类型下的学习行为，凸显了该平台在推动农业强化学习决策支持方面的潜力。
原文摘要 · Abstract (English)
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.
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