Optimizing High- Throughput Capabilities by Leveraging Reinforcement Learning Methods with the Bluesky Suite

Olds, Daniel; Allan, Daniel B.; Caswell, Thomas A.; Lynch, Joshua; Maffettone, Phillip M.; Campbell, Stuart I.

doi:10.1109/xloop54565.2021.00011

Optimizing High- Throughput Capabilities by Leveraging Reinforcement Learning Methods with the Bluesky Suite

Conference · 19 November 2021

DOI:https://doi.org/10.1109/xloop54565.2021.00011· OSTI ID:1974192

Olds, Daniel; Allan, Daniel B.; Caswell, Thomas A.; Lynch, Joshua; Maffettone, Phillip M.; Campbell, Stuart I.

Research Organization:: Brookhaven National Lab. (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

DOE Contract Number:: SC0012704

OSTI ID:: 1974192

Report Number(s):: BNL-224414-2023-COPA

Resource Relation:: Conference: 2021 3rd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP), St. Louis, MO, USA, 11/19/2021 - 11/19/2021

Country of Publication:: United States

Language:: English

References (13)

Bluesky's Ahead: A Multi-Facility Collaboration for an a la Carte Software Project for Data Acquisition and Management Allan, Daniel; Caswell, Thomas; Campbell, Stuart Synchrotron Radiation News, Vol. 32, Issue 3 https://doi.org/10.1080/08940886.2019.1608121	journal	May 2019
Matplotlib: A 2D Graphics Environment Hunter, John D. Computing in Science & Engineering, Vol. 9, Issue 3 https://doi.org/10.1109/MCSE.2007.55	journal	January 2007
Array programming with NumPy Harris, Charles R.; Millman, K. Jarrod; van der Walt, Stéfan J. Nature, Vol. 585, Issue 7825 https://doi.org/10.1038/s41586-020-2649-2	journal	September 2020
SciPy 1.0: fundamental algorithms for scientific computing in Python Virtanen, Pauli; Gommers, Ralf; Oliphant, Travis E. Nature Methods https://doi.org/10.1038/s41592-019-0686-2	journal	February 2020
Gaming the beamlines—employing reinforcement learning to maximize scientific outcomes at large-scale user facilities Maffettone, Phillip M.; Lynch, Joshua K.; Caswell, Thomas A. Machine Learning: Science and Technology, Vol. 2, Issue 2 https://doi.org/10.1088/2632-2153/abc9fc	journal	March 2021
Synchrotron X-ray Diffraction for Energy and Environmental Materials: The Current Role and Future Directions of Total Scattering Beamlines in the Functional Material Scientific Ecosystem Olds, Daniel Synchrotron Radiation News, Vol. 33, Issue 5 https://doi.org/10.1080/08940886.2020.1812352	journal	October 2020
Biped dynamic walking using reinforcement learning Benbrahim, Hamid; Franklin, Judy A. Robotics and Autonomous Systems, Vol. 22, Issue 3-4 https://doi.org/10.1016/S0921-8890(97)00043-2	journal	December 1997
Compressive Sensing for Dynamic XRF Scanning Kourousias, George; Billè, Fulvio; Borghes, Roberto Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-66435-6	journal	June 2020
A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering Noack, Marcus M.; Yager, Kevin G.; Fukuto, Masafumi Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-48114-3	journal	August 2019
Outlook for artificial intelligence and machine learning at the NSLS-II Campbell, Stuart I.; Allan, Daniel B.; Barbour, Andi M. Machine Learning: Science and Technology, Vol. 2, Issue 1 https://doi.org/10.1088/2632-2153/abbd4e	journal	March 2021
Mastering the game of Go without human knowledge Silver, David; Schrittwieser, Julian; Simonyan, Karen Nature, Vol. 550, Issue 7676 https://doi.org/10.1038/nature24270	journal	October 2017
Grandmaster level in StarCraft II using multi-agent reinforcement learning Vinyals, Oriol; Babuschkin, Igor; Czarnecki, Wojciech M. Nature, Vol. 575, Issue 7782 https://doi.org/10.1038/s41586-019-1724-z	journal	October 2019
Basic Reinforcement Learning Techniques to Control the Intensity of a Seeded Free-Electron Laser Bruchon, Niky; Fenu, Gianfranco; Gaio, Giulio Electronics, Vol. 9, Issue 5 https://doi.org/10.3390/electronics9050781	journal	May 2020

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