平流层气溶胶注入作为深度强化学习问题
Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem
摘要：
随着全球温室气体排放量继续上升，人们越来越多地考虑使用平流层气溶胶喷射（SAI），这是一种太阳地球工程，以人为地减轻气候变化的影响。然而，初步的模拟研究表明，幼稚的SAI可能会产生灾难性的区域后果，这可能会引发严重的地缘战略冲突。目前的地球工程研究对待SAI控制低维近似。我们建议将SAI视为一个高维控制问题，根据深度强化学习（DRL）范式中的上下文敏感奖励函数训练策略。为了便于模拟训练，我们建议使用深度学习技术模拟HadCM 3，这是一种广泛使用的大气环流模型。我们相信这是DRL在气候科学中的首次应用。
英文摘要：
As global greenhouse gas emissions continue to rise, the use of stratospheric aerosol injection (SAI), a form of solar geoengineering, is increasingly considered in order to artificially mitigate climate change effects. However, initial research in simulation suggests that naive SAI can have catastrophic regional consequences, which may induce serious geostrategic conflicts. Current geo-engineering research treats SAI control in low-dimensional approximation only. We suggest treating SAI as a high-dimensional control problem, with policies trained according to a context-sensitive reward function within the Deep Reinforcement Learning (DRL) paradigm. In order to facilitate training in simulation, we suggest to emulate HadCM3, a widely used General Circulation Model, using deep learning techniques. We believe this is the first application of DRL to the climate sciences.
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