Fetching the paper…
Reading the bibliography…
Reinforcement learning holds tremendous promise in accelerator controls.
1904
Earlier work this paper cites.
T. Wang and J. Ba, Exploring model-based planning with policy networks, (2019), 1906.08649
1906
Earlier work this paper cites.
1906
Earlier work this paper cites.
1907
Earlier work this paper cites.
R. S. Sutton, Dyna, an integrated architecture for learning, planning, and reacting, ACM SIGART Bulletin 2
1991
Earlier work this paper cites.
K. Furuta, M. Yamakita, and S. Kobayashi, Swing up control of inverted pendulum, in Proceedings IECON '91: 1991 International Conference on Industrial Electronics, Control and Instrumentation (IEEE, 1991)
1991
Earlier work this paper cites.
R. J. Williams, Simple statistical gradient-following algorithms for connectionist reinforcement learning, Machine Learning 8
1992
Earlier work this paper cites.
2005
Earlier work this paper cites.
P.-T. de Boer, D. P. Kroese, S. Mannor, and R. Y. Rubinstein, A tutorial on the cross-entropy method, Annals of Operations Research 134
2005
Earlier work this paper cites.
Y. Gu and D. S. Oliver, An iterative ensemble kalman filter for multiphase fluid flow data assimilation, SPE Journal 12
2007
Earlier work this paper cites.
2009
Earlier work this paper cites.
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
C. Szepesvári, Algorithms for reinforcement learning, Synthesis Lectures on Artificial Intelligence and Machine Learning 4
2010
Earlier work this paper cites.
H. Hasselt, Double q-learning, in Advances in Neural Information Processing Systems , Vol. 23, edited by J. Lafferty, C. Williams, J. Shawe-Taylor, R. Zemel, and A. Culotta (Curran Associates, Inc., 2010) pp. 2613–2621
2010
Earlier work this paper cites.
2011
Earlier work this paper cites.
M. P. Deisenroth and C. E. Rasmussen, Pilco: A model-based and data-efficient approach to policy search, in Proceedings of the 28th International Conference on International Conference on Machine Learning , ICML’11 (Omnipress, Madison, WI, USA, 2011) p. 465–472
2011
Earlier work this paper cites.
2011
Earlier work this paper cites.
Y. Chen and D. S. Oliver, Ensemble randomized maximum likelihood method as an iterative ensemble smoother, Mathematical Geosciences 44
2011
Earlier work this paper cites.
J. M. Bardsley, MCMC-based image reconstruction with uncertainty quantification, SIAM Journal on Scientific Computing 34
2012
Earlier work this paper cites.
X. Huang, J. Corbett, J. Safranek, and J. Wu, An algorithm for online optimization of accelerators, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 726
2013
Cited alongside, same era.
S. Levine and V. Koltun, Guided policy search, in Proceedings of the 30th International Conference on Machine Learning , Proceedings of Machine Learning Research, Vol. 28, edited by S. Dasgupta and D. McAllester (PMLR, Atlanta, Georgia, USA, 2013) pp. 1–9
2013
Cited alongside, same era.
2013
Cited alongside, same era.
D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Riedmiller, Deterministic policy gradient algorithms, in Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32 , ICML14 (JMLR.org, 2014) p. I–387–I–395
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
C. Welsch, Numerical optimization of accelerators within opac, Proceedings of the 6th Int. Particle Accelerator Conf. IPAC2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
2015
Cited alongside, same era.
M. P. Deisenroth, D. Fox, and C. E. Rasmussen, Gaussian processes for data-efficient learning in robotics and control, IEEE Transactions on Pattern Analysis and Machine Intelligence 37
2015
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
A. Hill, A. Raffin, M. Ernestus, A. Gleave, A. Kanervisto, R. Traore, P. Dhariwal, C. Hesse, O. Klimov, A. Nichol, M. Plappert, A. Radford, J. Schulman, S. Sidor, and Y. Wu, Stable baselines, https://github.com/hill-a/stable-baselines (2018)
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Hirlaender, M. Fraser, B. Goddard, V. Kain, J. Prieto, L. Stoel, M. Szakaly, and F. Velotti, Automatisation of the SPS ElectroStatic septa alignment, Proceedings of the 10th Int. Particle Accelerator Conf. IPAC2019
2019
Later among the works it cites.
S. Albright, R. Alemany Fernandez, M. E. Angoletta, H. Bartosik, A. Beaumont, G. Bellodi, N. Biancacci, M. Bozzolan, M. Buzio, F. Di Lorenzo, A. Frassier, D. Gamba, S. Hirlander, A. Huschauer, V. Kain, G. Kotzian, D. Kuchler, A. Latina, T. Levens, E. Mahner, E. Manosperti, O. Marqversen, D. Moreno Garcia, D. Nicosia, M. O’Neil, E. Ozturk, A. Saa Hernandez, R. Scrivens, S. Jensen, G. A. Tranquille, C. Wetton, and M. Zampetakis, Review of LEIR operation in 2018, (2019)
2019
Later among the works it cites.
N. Bruchon, G. Fenu, G. Gaio, M. Lonza, F. A. Pellegrino, and E. Salvato, Toward the application of reinforcement learning to the intensity control of a seeded free-electron laser, in 2019 23rd International Conference on Mechatronics Technology (ICMT) (IEEE, 2019)
2019
Later among the works it cites.
V. Kain, S. Hirlander, B. Goddard, F. M. Velotti, G. Z. D. Porta, N. Bruchon, and G. Valentino, Sample-efficient reinforcement learning for CERN accelerator control, Physical Review Accelerators and Beams 23
2020
Closest in time.
A. Scheinker, S. Hirlaender, F. M. Velotti, S. Gessner, G. Z. D. Porta, V. Kain, B. Goddard, and R. Ramjiawan, Online multi-objective particle accelerator optimization of the AWAKE electron beam line for simultaneous emittance and orbit control, AIP Advances 10
2020
Closest in time.
N. Bruchon, G. Fenu, G. Gaio, M. Lonza, F. H. O’Shea, F. A. Pellegrino, and E. Salvato, Basic reinforcement learning techniques to control the intensity of a seeded free-electron laser, Electronics 9
2020
Closest in time.
F. H. O’Shea, N. Bruchon, and G. Gaio, Policy gradient methods for free-electron laser and terahertz source optimization and stabilization at the FERMI free-electron laser at Elettra, Physical Review Accelerators and Beams 23
2020
Closest in time.
N. Bruchon, Feasibility Investigation on Several Reinforcement Learning Techniques to Improve the Performance of the FERMI Free-Electron Laser , PhD thesis, Università degli Studi di Trieste (2020), (unpublished)
2020
Closest in time.
S. Hirlaender, Mathphyssim/per-naf: Initial release, 10.5281/zenodo.4271647 (2020)
2020
Closest in time.
R. Kidambi, A. Rajeswaran, P. Netrapalli, and T. Joachims, Morel : model-based offline reinforcement learning, in NeurIPS 2020 (ACM, 2020)
2020
Closest in time.
S. Hirlaender and N. Bruchon, MathPhysSim/FERMI_RL_Paper: Initial release, 10.5281/ZENODO.4271580 (2020)
2020
Closest in time.
N. Bruchon, G. Fenu, G. Gaio, S. Hirlander, M. Lonza, F. A. Pellegrino, and E. Salvato, An online iterative linear quadratic approach for a satisfactory working point attainment at FERMI, Information 12
2021
Closest in time.