Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem

By Christian Schröder de Witt, Thomas Hornigold. 2019

9 citations recorded by the source corpus.

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.

Subfields: Astronomy and Astrophysics, Global and Planetary Change, Reinforcement Learning, AI for Physical Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Astronomy and Astrophysics

In Global and Planetary Change

In Reinforcement Learning

In AI for Physical Sciences

Citations in the corpus

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning (2020, background): “The applicability of QMIX in real-world application domains has also been considered such as robot swarm coordination (Httenrauch et al., 2018) and stratospheric geoengineering (de Witt and Hornigold, 2019).”
Tackling Climate Change with Machine Learning (2019, methodology): “[601] Christian Schroeder de Witt and Thomas Hornigold.”