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
15 papers
A view on learning robust goal-conditioned value functions: Interplay between RL and MPC
Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes +2
Reinforcement learning (RL) and model predictive control (MPC) offer a wealth of distinct approaches for automatic decision-making under uncertainty. Given the impact both fields h…
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…
The first large catalogue of spectroscopic redshifts in Webb's First Deep Field, SMACS J0723.37327
Gaël Noirot, Guillaume Desprez, Yoshihisa Asada +20
We present a spectroscopic redshift catalogue of the SMACS J0723.37327 field ("Webb's First Deep Field") obtained from JWST/NIRISS grism spectroscopy and supplemented with JWST/…
The Near Infrared Imager and Slitless Spectrograph for the James Webb Space Telescope -- IV. Aperture Masking Interferometry
Anand Sivaramakrishnan, Peter Tuthill, James P. Lloyd +36
The James Webb Space Telescope's Near Infrared Imager and Slitless Spectrograph (JWST-NIRISS) flies a 7-hole non-redundant mask (NRM), the first such interferometer in space, opera…
Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach
Daniel G. McClement, Nathan P. Lawrence, Johan U. Backstrom +3
Meta-learning is a branch of machine learning which trains neural network models to synthesize a wide variety of data in order to rapidly solve new problems. In process control, ma…
The Near Infrared Imager and Slitless Spectrograph for the James Webb Space Telescope -- II. Wide Field Slitless Spectroscopy
Chris J. Willott, René Doyon, Loic Albert +26
We present the wide field slitless spectroscopy mode of the NIRISS instrument on the James Webb Space Telescope. This mode employs two orthogonal low-resolution (resolving power $\…
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning
Nathan P. Lawrence, Michael G. Forbes, Philip D. Loewen +3
Deep reinforcement learning (RL) is an optimization-driven framework for producing control strategies for general dynamical systems without explicit reliance on process models. Goo…
Robotized polarization characterization platform for free-space quantum communication optics
Youn Seok Lee, Kimia Mohammadi, Lindsay Babcock +3
We develop a polarization characterization platform for optical devices in free-space quantum communications. We demonstrate an imaging polarimeter, which analyzes both incident po…
A Meta-Reinforcement Learning Approach to Process Control
Daniel G. McClement, Nathan P. Lawrence, Philip D. Loewen +3
Meta-learning is a branch of machine learning which aims to quickly adapt models, such as neural networks, to perform new tasks by learning an underlying structure across related t…
Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem
Nathan P. Lawrence, Gregory E. Stewart, Philip D. Loewen +3
Deep reinforcement learning (DRL) has seen several successful applications to process control. Common methods rely on a deep neural network structure to model the controller or pro…
Reinforcement Learning based Design of Linear Fixed Structure Controllers
Nathan P. Lawrence, Gregory E. Stewart, Philip D. Loewen +3
Reinforcement learning has been successfully applied to the problem of tuning PID controllers in several applications. The existing methods often utilize function approximation, su…