490 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of British ColumbiaCA9 papers
- Herzberg Institute of AstrophysicsCA5 papers
- Space Telescope Science InstituteUS5 papers
- Université de MontréalCA5 papers
- Cornell UniversityUS4 papers
- University of MichiganUS4 papers
- American Museum of Natural HistoryUS3 papers
- Canadian Institute for Theoretical AstrophysicsCA3 papers
- Canadian Space AgencyCA3 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- Goddard Space Flight CenterUS3 papers
- Johns Hopkins UniversityUS3 papers
5 papers · 1 filter
Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior
Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang +2
We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guar…
The Near Infrared Imager and Slitless Spectrograph for the James Webb Space Telescope -- III. Single Object Slitless Spectroscopy
Loic Albert, David Lafreniere, Rene Doyon +37
The Near Infrared Imager and Slitless Spectrograph instrument (NIRISS) is the Canadian Space Agency (CSA) contribution to the suite of four science instruments of JWST. As one of t…
Reinforcement Learning with Partial Parametric Model Knowledge
Shuyuan Wang, Philip D. Loewen, Nathan P. Lawrence +2
We adapt reinforcement learning (RL) methods for continuous control to bridge the gap between complete ignorance and perfect knowledge of the environment. Our method, Partial Knowl…
The James Webb Space Telescope Mission
Jonathan P. Gardner, John C. Mather, Randy Abbott +1004
Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized…
A modular framework for stabilizing deep reinforcement learning control
Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang +2
We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guar…