Logo

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

Small book cover: Reinforcement Learning

Reinforcement Learning
by

Publisher: InTech
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.

Home page url

Download or read it online for free here:
Download link
(12MB, PDF)

Similar books

Book cover: Inductive Logic Programming: Theory and MethodsInductive Logic Programming: Theory and Methods
by - ScienceDirect
Inductive Logic Programming is a new discipline which investigates the inductive construction of first-order clausal theories from examples and background knowledge. The authors survey the most important theories and methods of this new field.
(28500 views)
Book cover: Practical Artificial Intelligence Programming in JavaPractical Artificial Intelligence Programming in Java
by - Lulu.com
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).
(19444 views)
Book cover: Learning Deep Architectures for AILearning Deep Architectures for AI
by - Now Publishers
This book discusses the principles of learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models.
(4145 views)
Book cover: Elements of Causal Inference: Foundations and Learning AlgorithmsElements of Causal Inference: Foundations and Learning Algorithms
by - The MIT Press
This book offers a self-contained and concise introduction to causal models and how to learn them from data. The book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from data ...
(2430 views)