Key Features

• Comprehensive introduction to reinforcement learning and machine learning concepts
• Covers core topics like Markov decision processes, Q-learning, and policy gradients
• Includes practical examples and algorithms used in real-world AI applications
• Ideal for students, researchers, and developers in artificial intelligence

Book Description

This book is a foundational resource for understanding reinforcement learning, one of the most important areas in artificial intelligence. It explains how agents learn to make decisions by interacting with environments to maximize rewards over time.

The content combines theoretical concepts with practical algorithms, helping readers understand both the mathematics and implementation of reinforcement learning techniques. It covers essential topics such as dynamic programming, Monte Carlo methods, and temporal-difference learning.

Widely used in universities and research, this book is suitable for beginners as well as advanced learners looking to deepen their knowledge of AI. It provides clear explanations, examples, and insights into modern machine learning applications.

Overall, this book is an essential guide for mastering reinforcement learning and building intelligent systems.

Product Details

• Title: Reinforcement Learning: An Introduction
• Author: Richard S. Sutton, Andrew G. Barto
• Publisher: MIT Press
• Language: English
• Binding: Paperback
• Number of Pages: 552 pages
• ISBN-10: 0262039249
• ISBN-13: 978-0262039246
• Publisher Date: October 2018 (2nd Edition)
• Weight: 2.0 pounds

Physical Specifications

• Height: 9.20 inches
• Width: 7.30 inches
• Spine Width: 1.20 inches