基于高维传感器反馈的深度强化学习灌溉调度
Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedback
AI总结：
本文提出一个基于深度强化学习的灌溉调度框架，利用高维传感器反馈，在澳大利亚小麦案例中实现利润最大化，决策规则在2011-2020年测试中一致优于常规规则，最大改进17%。
摘要：
深度强化学习在多种种植系统中具有巨大潜力，通过基于随时间变化的各种测量值自适应地施加水量，来改进灌溉调度。其目标是发现一种智能决策规则，该规则处理种植者可获得的信息，并为所考虑的时间步长规定合理的灌溉量。然而，由于技术新颖性，该技术的研究仍然稀少且不切实际。为了加速进展，本文提出了一个原则性的框架和可操作的程序，使研究人员能够构建自己的优化问题，并基于深度强化学习实现求解算法。该框架的有效性通过一个案例研究得以证明，该案例研究针对澳大利亚一个生产区种植的灌溉小麦，目标是利润最大化。具体来说，决策规则接受九个状态变量输入：作物物候阶段、叶面积指数、前五层土壤的可用水量、累积降雨量和累积灌溉量。它每天返回五个候选灌溉量（0、10、20、30和40毫米）的概率性处方。生产系统在Goondiwindi使用APSIM-Wheat作物模型进行模拟。在使用1981-2010年天气数据的学习环境中训练后，学习到的决策规则在2011-2020年每年单独进行测试。结果与通过该地区常见常规规则获得的基准利润进行了比较。所发现的决策规则在所有测试年份中规定的每日灌溉量均一致优于常规规则，其中2018年的最大改进达到17%。该框架具有通用性，适用于具有现实优化问题的广泛种植系统。
英文摘要：
Deep reinforcement learning has considerable potential to improve irrigation scheduling in many cropping systems by applying adaptive amounts of water based on various measurements over time. The goal is to discover an intelligent decision rule that processes information available to growers and prescribes sensible irrigation amounts for the time steps considered. Due to the technical novelty, however, the research on the technique remains sparse and impractical. To accelerate the progress, the paper proposes a principled framework and actionable procedure that allow researchers to formulate their own optimisation problems and implement solution algorithms based on deep reinforcement learning. The effectiveness of the framework was demonstrated using a case study of irrigated wheat grown in a productive region of Australia where profits were maximised. Specifically, the decision rule takes nine state variable inputs: crop phenological stage, leaf area index, extractable soil water for each of the five top layers, cumulative rainfall and cumulative irrigation. It returns a probabilistic prescription over five candidate irrigation amounts (0, 10, 20, 30 and 40 mm) every day. The production system was simulated at Goondiwindi using the APSIM-Wheat crop model. After training in the learning environment using 1981-2010 weather data, the learned decision rule was tested individually for each year of 2011-2020. The results were compared against the benchmark profits obtained by a conventional rule common in the region. The discovered decision rule prescribed daily irrigation amounts that uniformly improved on the conventional rule for all the testing years, and the largest improvement reached 17% in 2018. The framework is general and applicable to a wide range of cropping systems with realistic optimisation problems.
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