论文标题
Gym-DSSAT：农作物模型变成了增强学习环境
gym-DSSAT: a crop model turned into a Reinforcement Learning environment
论文作者
论文摘要
通过加强学习解决现实世界的顺序决策问题（RL）通常始于使用模仿真实条件的模拟环境。我们为现实的农作物管理任务提供了一种新颖的开源RL环境。 Gym-DSSAT是高保真作物模拟器的Agrotechnology Transper（DSSAT）的决策支持系统的健身房界面。在过去的30年中，DSSAT已发展，并被农学家广泛认可。 Gym-DSSAT带有基于现实世界玉米实验的预定义仿真。环境与任何健身房环境一样易于使用。我们使用基本RL算法提供性能基准。我们还简要概述了用Fortran编写的单片DSSAT模拟器如何变成Python RL环境。我们的方法是通用的，可以应用于类似的模拟器。我们报告了非常初步的实验结果，这表明RL可以帮助研究人员改善受精和灌溉实践的可持续性。
Addressing a real world sequential decision problem with Reinforcement Learning (RL) usually starts with the use of a simulated environment that mimics real conditions. We present a novel open source RL environment for realistic crop management tasks. gym-DSSAT is a gym interface to the Decision Support System for Agrotechnology Transfer (DSSAT), a high fidelity crop simulator. DSSAT has been developped over the last 30 years and is widely recognized by agronomists. gym-DSSAT comes with predefined simulations based on real world maize experiments. The environment is as easy to use as any gym environment. We provide performance baselines using basic RL algorithms. We also briefly outline how the monolithic DSSAT simulator written in Fortran has been turned into a Python RL environment. Our methodology is generic and may be applied to similar simulators. We report on very preliminary experimental results which suggest that RL can help researchers to improve sustainability of fertilization and irrigation practices.
