本文提出一种半集中式多智能体强化学习（SCMARL，Semi-Centralized Multi-Agent Reinforcement Learning）方法，用于空间异质农田的灌溉调度，其中通过管理分区来应对空间变异性。SCMARL框架本质上是层级式的，顶层为集中式协调智能体，第二层为分布式本地智能体。协调智能体基于全田条件做出每日二元灌溉决策，并将决策传达给本地智能体。本地智能体利用局部条件为特定管理分区确定合适的灌溉量。该框架采用状态增强方法来处理本地智能体环境中的非平稳性。研究在加拿大莱斯布里奇的一块大规模农田上开展了全面评估，将SCMARL方法与基于学习的多智能体模型预测控制调度方法进行对比，凸显了其更优的性能，可实现节水并提升灌溉水利用效率（IWUE，Irrigation Water Use Efficiency）。值得注意的是，所提方法实现了4.0%的灌溉水节约，同时将IWUE提升了6.3%。
英文摘要：
This paper proposes a Semi-Centralized Multi-Agent Reinforcement Learning (SCMARL) approach for irrigation scheduling in spatially variable agricultural fields, where management zones address spatial variability. The SCMARL framework is hierarchical in nature, with a centralized coordinator agent at the top level and decentralized local agents at the second level. The coordinator agent makes daily binary irrigation decisions based on field-wide conditions, which are communicated to the local agents. Local agents determine appropriate irrigation amounts for specific management zones using local conditions. The framework employs state augmentation approach to handle non-stationarity in the local agents' environments. An extensive evaluation on a large-scale field in Lethbridge, Canada, compares the SCMARL approach with a learning-based multi-agent model predictive control scheduling approach, highlighting its enhanced performance, resulting in water conservation and improved Irrigation Water Use Efficiency (IWUE). Notably, the proposed approach achieved a 4.0% savings in irrigation water while enhancing the IWUE by 6.3%.
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