Learn IT - Books tags reinforcement

TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

O'Reilly, 2018

Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects...

Cells and Robots: Modeling and Control of Large-Size Agent Populations (Springer Tracts in Advanced Robotics)

Springer, 2007

From the reviews:

"This interesting monograph is devoted to the study of multi-agent, populations of biological cells and robot teams. … The main objective of the approach is to provide solutions to the problem of how dynamics of an individual agent propagates to the population dynamics. … This original monograph has a wide...

Reinforcement Learning: State-of-the-Art (Adaptation, Learning, and Optimization)

Springer, 2012

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the...

Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

O'Reilly, 2017

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.

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Mobile Robots: The Evolutionary Approach (Studies in Computational Intelligence)

Springer, 2007

The design and control of autonomous intelligent mobile robotic systems operating in unstructured changing environments includes many objective difficulties. There are several studies about the ways in which, robots exhibiting some degree of autonomy, adapt themselves to fit in their environments. The application and use of bio-inspired and...

Auralization: Fundamentals of Acoustics, Modelling, Simulation, Algorithms and Acoustic Virtual Reality (RWTHedition)

Springer, 2007

From the reviews:

"Particularly attractive about this book is its very complete coverage of auralization techniques, presenting applications not only in room acoustics but also in sound insulation and in other noise control engineering applications, as well as in real-time virtual reality. … The book serves as an excellent overview...

Learning in Embedded Systems (Bradford Books)

MIT Press, 1993

Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first...

Computability and Complexity Theory (Texts in Computer Science)

Springer, 2011

This revised and extensively expanded edition of Computability and Complexity Theory comprises essential materials that are core knowledge in the theory of computation. The book is self-contained, with a preliminary chapter describing key mathematical concepts and notations. Subsequent chapters move from the qualitative aspects of...

AI Game Programming Wisdom 4 (AI Game Programming Wisdom (W/CD))

Charles River, 2008

Welcome to the latest volume of AI Game Programming Wisdom! AI Game Programming Wisdom 4 includes a collection of more than 50 new articles featuring cutting-edge techniques, algorithms, and architectures written by industry professionals for use in commercial game development. Organized into 7 sections, this comprehensive volume explores...

Qualitative Spatial Abstraction in Reinforcement Learning (Cognitive Technologies)

Springer, 2010

Teaching and learning are difficult tasks not only when people are involved but also with regard to computer programs and machines: When the teaching/learning units are too small, we cannot express sufficient context to teach a differentiated lesson; when they are too large, the complexity of the learning task can increase...

Foundations of Learning Classifier Systems (Studies in Fuzziness and Soft Computing)

Springer, 2005

Learning Classifier Systems (LCS) [Holland, 1976] are a machine learning technique which combines evolutionary computing, reinforcement learning, supervised learning or unsupervised learning, and heuristics to produce adaptive systems. They are rulebased systems, where the rules are usually in the traditional production system form of “IF...

Data Structures and Algorithms in Python

John Wiley & Sons, 2013

Based on the authors’ market leading data structures books in Java and C++, this book offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures....

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