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Autonomous navigation of stratospheric balloons using reinforcement learning

Author

Listed:
  • Marc G. Bellemare

    (Brain Team, Google Research)

  • Salvatore Candido

    (Loon)

  • Pablo Samuel Castro

    (Brain Team, Google Research)

  • Jun Gong

    (Loon)

  • Marlos C. Machado

    (Brain Team, Google Research)

  • Subhodeep Moitra

    (Brain Team, Google Research)

  • Sameera S. Ponda

    (Loon)

  • Ziyu Wang

    (Brain Team, Google Research)

Abstract

Efficiently navigating a superpressure balloon in the stratosphere1 requires the integration of a multitude of cues, such as wind speed and solar elevation, and the process is complicated by forecast errors and sparse wind measurements. Coupled with the need to make decisions in real time, these factors rule out the use of conventional control techniques2,3. Here we describe the use of reinforcement learning4,5 to create a high-performing flight controller. Our algorithm uses data augmentation6,7 and a self-correcting design to overcome the key technical challenge of reinforcement learning from imperfect data, which has proved to be a major obstacle to its application to physical systems8. We deployed our controller to station Loon superpressure balloons at multiple locations across the globe, including a 39-day controlled experiment over the Pacific Ocean. Analyses show that the controller outperforms Loon’s previous algorithm and is robust to the natural diversity in stratospheric winds. These results demonstrate that reinforcement learning is an effective solution to real-world autonomous control problems in which neither conventional methods nor human intervention suffice, offering clues about what may be needed to create artificially intelligent agents that continuously interact with real, dynamic environments.

Suggested Citation

  • Marc G. Bellemare & Salvatore Candido & Pablo Samuel Castro & Jun Gong & Marlos C. Machado & Subhodeep Moitra & Sameera S. Ponda & Ziyu Wang, 2020. "Autonomous navigation of stratospheric balloons using reinforcement learning," Nature, Nature, vol. 588(7836), pages 77-82, December.
  • Handle: RePEc:nat:nature:v:588:y:2020:i:7836:d:10.1038_s41586-020-2939-8
    DOI: 10.1038/s41586-020-2939-8
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    Citations

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    Cited by:

    1. Biemann, Marco & Scheller, Fabian & Liu, Xiufeng & Huang, Lizhen, 2021. "Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control," Applied Energy, Elsevier, vol. 298(C).
    2. Jinming Xu & Yuan Lin, 2024. "Energy Management for Hybrid Electric Vehicles Using Safe Hybrid-Action Reinforcement Learning," Mathematics, MDPI, vol. 12(5), pages 1-20, February.
    3. Constantin Waubert de Puiseau & Richard Meyes & Tobias Meisen, 2022. "On reliability of reinforcement learning based production scheduling systems: a comparative survey," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 911-927, April.
    4. Wu, Jingda & Huang, Chao & He, Hongwen & Huang, Hailong, 2024. "Confidence-aware reinforcement learning for energy management of electrified vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    5. Malte Reinschmidt & József Fortágh & Andreas Günther & Valentin V. Volchkov, 2024. "Reinforcement learning in cold atom experiments," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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