Posted by **rolexmaya** at Aug. 19, 2011

Springer; 1st Edition | October 6, 2010 | ISBN-10: 3642139310 | 133 pages | PDF | 4.17 Mb

This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task.

Posted by **step778** at May 22, 2017

2014 | pages: 196 | ISBN: 3319121960 | PDF | 5,7 mb

Posted by **advisors** at March 4, 2015

2015 | 208 Pages | ISBN: 3319121960 | PDF | 6 MB

Posted by **interes** at Nov. 16, 2014

English | 2014 | ISBN: 1489974903 | 508 pages | PDF | 5 MB

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms.

Posted by **step778** at Sept. 16, 2015

1998 | pages: 331 | ISBN: 0262193981 | PDF | 2,3 mb

Posted by **ChrisRedfield** at July 24, 2015

Published: 2013-07-04 | ISBN: 3319011677, 3319011693 | PDF | 165 pages | 5.94 MB

Posted by **interes** at April 2, 2015

English | ISBN: 3319011677 | 2013 | 200 pages | PDF | 6 MB

Posted by **Maroutan** at March 19, 2015

English | 2012 | 309 Pages | ISBN: 1447142845 | PDF | 4.5 MB

Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented…

Posted by **bookwyrm** at Dec. 26, 2014

2013 | 309 Pages | ISBN: 1447142845 | PDF | 5 MB

Posted by **nebulae** at Oct. 31, 2014

English | ISBN: 3319011677 | 2013 | 200 pages | PDF | 6 MB