Deep Reinforcement Learning : Frontiers of Artificial Intelligence / by Mohit Sewak.

Format:

Book

Author/Creator:

Sewak, Mohit, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Computer programming.

Artificial intelligence.

Algorithms.

Data encryption (Computer science).

Programming Techniques.

Artificial Intelligence.

Algorithm Analysis and Problem Complexity.

Cryptology.

Local Subjects:

Programming Techniques.

Artificial Intelligence.

Algorithm Analysis and Problem Complexity.

Cryptology.

Physical Description:

1 online resource (XVII, 203 pages) : 106 illustrations, 98 illustrations in color

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Singapore : Springer Singapore : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code. This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds - deep learning and reinforcement learning - to tap the potential of 'advanced artificial intelligence' for creating real-world applications and game-winning algorithms.

Contents:

Introduction to Reinforcement Learning

Mathematical and Algorithmic understanding of Reinforcement Learning

Coding the Environment and MDP Solution

Temporal Difference Learning, SARSA, and Q Learning

Q Learning in Code

Introduction to Deep Learning

Implementation Resources

Deep Q Network (DQN), Double DQN and Dueling DQN

Double DQN in Code

Policy-Based Reinforcement Learning Approaches

Actor-Critic Models and the A3C

A3C in Code

Deterministic Policy Gradient and the DDPG

DDPG in Code.

Other Format:

Printed edition:

ISBN:

978-981-13-8285-7

9789811382857

9789811382840

9789811382864

9789811382871

Access Restriction:

Restricted for use by site license.