捆绑SM社区

NCRN Seminar

Speaker: Marc G. Bellemare, Google Brain in Montreal

Zoom:

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ABSTRACT:
Efficiently navigating a superpressure balloon in the stratosphere 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 techniques. This talk describes the use of reinforcement learning to create a high-performing flight controller for Loon superpressure balloons. Our algorithm uses data augmentation 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 systems. We deployed our controller to station Loon 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.

BIOGRAPHY:
Marc G. Bellemare leads the reinforcement learning efforts at Google Brain in Montreal and holds a Canada CIFAR AI Chair at Mila. He received his Ph.D. from the University of Alberta, where he developed the highly-successful Arcade Learning Environment benchmark. From 2013 to 2017 he worked at DeepMind in London, UK, where he made major contributions to deep reinforcement learning, in particular pioneering the distributional method. Marc G. Bellemare is also a CIFAR Learning in Machines & Brains Fellow and an adjunct professor at 捆绑SM社区.