**Reinforcement Learning**

by C. Weber, M. Elshaw, N. M. Mayer

**Publisher**: InTech 2008**ISBN-13**: 9783902613141**Number of pages**: 424

**Description**:

The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels.

Download or read it online for free here:

**Download link**

(12MB, PDF)

## Similar books

**An Introductory Study on Time Series Modeling and Forecasting**

by

**Ratnadip Adhikari, R. K. Agrawal**-

**arXiv**

This work presents a concise description of some popular time series forecasting models used in practice, with their features. We describe three important classes of time series models, viz. the stochastic, neural networks and SVM based models.

(

**12269**views)

**Introduction to Machine Learning for the Sciences**

by

**Titus Neupert, et al.**-

**arXiv.org**

This is an introductory machine learning course specifically developed with STEM students in mind, written by the theoretical Condensed Matter Theory group at the University of Zurich. We discuss supervised, unsupervised, and reinforcement learning.

(

**3416**views)

**The Elements of Statistical Learning: Data Mining, Inference, and Prediction**

by

**T. Hastie, R. Tibshirani, J. Friedman**-

**Springer**

This book brings together many of the important new ideas in learning, and explains them in a statistical framework. The authors emphasize the methods and their conceptual underpinnings rather than their theoretical properties.

(

**40884**views)

**Introduction To Machine Learning**

by

**Nils J Nilsson**

This book concentrates on the important ideas in machine learning, to give the reader sufficient preparation to make the extensive literature on machine learning accessible. The author surveys the important topics in machine learning circa 1996.

(

**30182**views)