Results 221 to 230 of about 73,043 (272)
Principled reward shaping for reinforcement learning via lyapunov stability theory
Neurocomputing, 2020 Abstract Reinforcement learning (RL) suffers from the designation in reward function and the large computational iterating steps until convergence. How to accelerate the training process in RL plays a vital role. In this paper, we proposed a Lyapunov function based approach to shape the reward function which can effectively accelerate the training ...
Yunlong Dong, Xiuchuan Tang, Ye Yuan
openalex +2 more sources
A NOTE ON STABILITY OF ERROR BOUNDS IN STATISTICAL LEARNING THEORY
Analysis and Applications, 2011 We consider a wide class of error bounds developed in the context of statistical learning theory which are expressed in terms of functionals of the regression function, for instance, its norm in a reproducing kernel Hilbert space or other functional space.
Ming Li, Andrea Caponnetto
openalex +3 more sources
Experimentally verified Iterative Learning Control based on repetitive process stability theory
2012 American Control Conference (ACC), 2012 This paper gives new results on the design and experimental evaluation of an Iterative Learning Control (ILC) law in a repetitive process setting. The experimental results given are from a gantry robot facility that has been extensively used in the benchmarking of linear model based ILC designs.
Paweł Dąbkowski +7 more
+4 more sources
Stability Certificates for Neural Network Learning-based Controllers using Robust Control Theory
2021 American Control Conference (ACC), 2021 Providing stability guarantees for controllers that use neural networks can be challenging. Robust control theoretic tools are used to derive a framework for providing nominal stability guarantees – stability guarantees for a known nominal system – controlled by a learning-based neural network controller.
Nguyen Hoang Hai +5 more
openalex +2 more sources
STABILITY RESULTS IN LEARNING THEORY
Analysis and Applications, 2005 The problem of proving generalization bounds for the performance of learning algorithms can be formulated as a problem of bounding the bias and variance of estimators of the expected error. We show how various stability assumptions can be employed for this purpose.
Alexander Rakhlin +2 more
openalex +3 more sources
Physical Chemistry Chemical Physics, 2022 Composition, size, and structure dependences of stable configuration of Pd–Ru alloy nanoparticles under finite temperature were theoretically investigated by using density functional theory calculation, multiple regression, and Wang–Landau sampling.
Yūsuke Nanba, Michihisa Koyama
openalex +3 more sources
Chemistry of Materials, 2017 We perform a large scale benchmark of machine learning methods for the prediction of the thermodynamic stability of solids. We start by constructing a data set that comprises density functional theory calculations of around 250000 cubic perovskite systems.
Jonathan Schmidt +5 more
openalex +2 more sources
The Journal of Chemical Physics, 2018 We use a combination of machine learning techniques and high-throughput density-functional theory calculations to explore ternary compounds with the AB2C2 composition. We chose the two most common intermetallic prototypes for this composition, namely, the tI10-CeAl2Ga2 and the tP10-FeMo2B2 structures.
Jonathan Schmidt +3 more
openalex +3 more sources
Advances in Computational Mathematics, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sayan Mukherjee +3 more
openalex +2 more sources