A minimization problem in residual current modelling

Finite-sample Analysis of Bellman Residual Minimization

We consider the Bellman residual minimization approach for solving discounted Markov decision problems, where we assume that a generative model of the dynamics and rewards is available. At each policy iteration step, an approximation of the value function for the current policy is obtained by minimizing an empirical Bellman residual defined on a set of n states drawn i.i.d. from a distribution ...

Residual current devices in LV

«Cahiers Techniques» is a collection of documents intended for engineers and technicians, people in the industry who are looking for more in-depth information in order to complement that given in product catalogues. Furthermore, these «Cahiers Techniques» are often considered as helpful «tools» for training courses. They provide knowledge on new technical and technological developments in the e...

Image Denoising via Residual Kurtosis Minimization

A new algorithm for the removal of additive uncorrelated Gaussian noise from a digital image is presented. The algorithm is based on a data driven methodology for the adaptive thresholding of wavelet coefficients. This methodology is derived from higher order statistics of the residual image, and requires no a priori estimate of the level of noise contamination of an image.

Boosted Bellman Residual Minimization Handling Expert Demonstrations

This paper addresses the problem of batch Reinforcement Learning with Expert Demonstrations (RLED). In RLED, the goal is to find an optimal policy of a Markov Decision Process (MDP), using a data set of fixed sampled transitions of the MDP as well as a data set of fixed expert demonstrations. This is slightly different from the batch Reinforcement Learning (RL) framework where only fixed sample...

On Grids and Solutions from Residual Minimization