论文标题
通过深厚的增强学习和作物模拟优化氮管理
Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations
论文作者
论文摘要
氮（N）的管理对于维持土壤生育能力和作物生产至关重要，同时最大程度地减少了负面影响，但要优化挑战。本文提出了一个智能的N管理系统，使用深度强化学习（RL）和农作物仿真，并使用农业技术的决策支持系统（DSSAT）。我们首先将N管理问题提出为RL问题。然后，我们使用深层Q网络和软演员批评算法以及Gym-DSSAT界面进行培训管理政策，该界面允许每天在模拟的农作物环境和RL代理之间进行互动。根据美国爱荷华州和佛罗里达州玉米作物的实验，我们的RL训练政策通过实现较高或类似的收益率优于先前的经验方法，而使用较少的肥料
Nitrogen (N) management is critical to sustain soil fertility and crop production while minimizing the negative environmental impact, but is challenging to optimize. This paper proposes an intelligent N management system using deep reinforcement learning (RL) and crop simulations with Decision Support System for Agrotechnology Transfer (DSSAT). We first formulate the N management problem as an RL problem. We then train management policies with deep Q-network and soft actor-critic algorithms, and the Gym-DSSAT interface that allows for daily interactions between the simulated crop environment and RL agents. According to the experiments on the maize crop in both Iowa and Florida in the US, our RL-trained policies outperform previous empirical methods by achieving higher or similar yield while using less fertilizers
