Zobrazeno 1 - 10
of 594
pro vyhledávání: '"Forbes, Michael A."'
Autor:
Sippel, Lucas, Forbes, Michael
The method of fragments was recently proposed, and its effectiveness has been empirically shown for three specialised pickup and delivery problems. We propose an enhanced fragment algorithm that for the first time, effectively solves the Pickup and D
Externí odkaz:
http://arxiv.org/abs/2411.13151
Autor:
Wang, Shuyuan, Duan, Jingliang, Lawrence, Nathan P., Loewen, Philip D., Forbes, Michael G., Gopaluni, R. Bhushan, Zhang, Lixian
Model-free reinforcement learning (RL) is inherently a reactive method, operating under the assumption that it starts with no prior knowledge of the system and entirely depends on trial-and-error for learning. This approach faces several challenges,
Externí odkaz:
http://arxiv.org/abs/2410.16821
Autor:
Lawrence, Nathan P., Loewen, Philip D., Wang, Shuyuan, Forbes, Michael G., Gopaluni, R. Bhushan
Willems' fundamental lemma enables a trajectory-based characterization of linear systems through data-based Hankel matrices. However, in the presence of measurement noise, we ask: Is this noisy Hankel-based model expressive enough to re-identify itse
Externí odkaz:
http://arxiv.org/abs/2404.15512
Autor:
Lawrence, Nathan P., Loewen, Philip D., Wang, Shuyuan, Forbes, Michael G., Gopaluni, R. Bhushan
Publikováno v:
Automatica 2024
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 guarantees provided by using the Youla-Kucera parameterization to define t
Externí odkaz:
http://arxiv.org/abs/2310.14098
Autor:
Wlazlowski, Gabriel, Forbes, Michael McNeil, Sarkar, Saptarshi Rajan, Marek, Andreas, Szpindler, Maciej
Publikováno v:
PNAS Nexus 3, pgae160 (2024)
Ultracold atoms provide a platform for analog quantum computer capable of simulating the quantum turbulence that underlies puzzling phenomena like pulsar glitches in rapidly spinning neutron stars. Unlike other platforms like liquid helium, ultracold
Externí odkaz:
http://arxiv.org/abs/2310.03341
Autor:
Jeuken, Rick, Forbes, Michael
This article describes a novel approach to chance-constrained programming based on the sample average approximation (SAA) method. Recent work focuses on heuristic approximations to the SAA problem and we introduce a novel approach which improves on s
Externí odkaz:
http://arxiv.org/abs/2307.12443
We construct an efficient parameterization of the pure neutron-matter equation of state (EoS) that incorporates the uncertainties from both chiral effective field theory ($\chi$EFT) and phenomenological potential calculations. This parameterization y
Externí odkaz:
http://arxiv.org/abs/2306.04386
Autor:
Wang, Shuyuan, Loewen, Philip D., Lawrence, Nathan P., Forbes, Michael G., Gopaluni, R. Bhushan
Publikováno v:
IFAC-PapersOnLine 2023
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 Knowledge Least Squares Policy Iteration (PLSPI), takes inspiration from bo
Externí odkaz:
http://arxiv.org/abs/2304.13223
Autor:
Lawrence, Nathan P., Loewen, Philip D., Wang, Shuyuan, Forbes, Michael G., Gopaluni, R. Bhushan
Publikováno v:
IFAC-PapersOnLine 2023
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 guarantees provided by using the Youla-Kucera parameterization to define t
Externí odkaz:
http://arxiv.org/abs/2304.03422
Autor:
McClement, Daniel G., Lawrence, Nathan P., Forbes, Michael G., Loewen, Philip D., Backström, Johan U., Gopaluni, R. Bhushan
Meta-learning is a branch of machine learning which aims to synthesize data from a distribution of related tasks to efficiently solve new ones. In process control, many systems have similar and well-understood dynamics, which suggests it is feasible
Externí odkaz:
http://arxiv.org/abs/2209.09301