Summary of Using Reinforcement Learning to Improve Drone-based Inference Of Greenhouse Gas Fluxes, by Alouette Van Hove et al.
Using reinforcement learning to improve drone-based inference of greenhouse gas fluxes
by Alouette van Hove, Kristoffer Aalstad, Norbert Pirk
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO); Atmospheric and Oceanic Physics (physics.ao-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study presents a novel framework for estimating surface greenhouse gas fluxes using drones, which is essential for validating and calibrating climate models. The approach combines data assimilation (DA) to infer fluxes from drone-based observations with reinforcement learning (RL) to optimize the drone’s sampling strategy. The research demonstrates that a RL-trained drone can accurately quantify CO2 hotspots more effectively than one following a predetermined flight path. The study also explores various reward functions, including information-based and error-based methods, which can drive the drone’s actions to increase its confidence in updated beliefs without requiring knowledge of the true surface flux. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses drones to measure greenhouse gas emissions on the Earth’s surface. It helps us understand where gases like carbon dioxide are coming from. The researchers combined two techniques: one that looks at data and another that learns by trial and error. They found that a drone that follows a planned route can be improved upon by using a drone that changes its route based on what it sees. This could help us get better measurements in the future. |
Keywords
* Artificial intelligence * Reinforcement learning
