Price loading...

The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python

AI & Machine Learning

Price data last checked 107 day(s) ago - refreshing...

View at Amazon

Price History & Forecast

No Price Data Available

Price history will appear here once data is collected from Amazon.

Price Distribution

No price data available for histogram

Description

Unlock the full potential of reinforcement learning (RL), a crucial subfield of Artificial Intelligence, with this comprehensive guide. This book provides a deep dive into RL's core concepts, mathematics, and practical algorithms, helping you to develop a thorough understanding of this cutting-edge technology. Beginning with an overview of fundamental concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, and temporal difference learning, this book uses clear and concise examples to explain the basics of RL theory. The following section covers value function approximation, a critical technique in RL, and explores various policy approximations such as policy gradient methods and advanced algorithms like Proximal Policy Optimization (PPO). This book also delves into advanced topics, including distributed reinforcement learning, curiosity-driven exploration, and the famous AlphaZero algorithm, providing readers with a detailed account of these cutting-edge techniques. With a focus on explaining algorithms and the intuition behind them, The Art of Reinforcement Learning includes practical source code examples that you can use to implement RL algorithms. Upon completing this book, you will have a deep understanding of the concepts, mathematics, and algorithms behind reinforcement learning, making it an essential resource for AI practitioners, researchers, and students. What You Will Learn Grasp fundamental concepts and distinguishing features of reinforcement learning, including how it differs from other AI and non-interactive machine learning approaches Model problems as Markov decision processes, and how to evaluate and optimize policies using dynamic programming, Monte Carlo methods, and temporal difference learning Utilize techniques for approximating value functions and policies, including linear and nonlinear value function approximation and policy gradient methods Understand the architecture and advantages of distributed reinforcement learning Master the concept of curiosity-driven exploration and how it can be leveraged to improve reinforcement learning agents Explore the AlphaZero algorithm and how it was able to beat professional Go players Who This Book Is For Machine learning engineers, data scientists, software engineers, and developers who want to incorporate reinforcement learning algorithms into their projects and applications.

Product Specifications

Format: Paperback
ASIN: 1484296052
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 09 December 2023
Listed Since: 16 May 2023

Barcode

No barcode data available

The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python

Price History & Forecast

No Price Data Available

Price Distribution

Description

Product Specifications

Barcode

Similar Products You Might Like

Reinforcement Learning for Sequential Decision and Optimal Control

Reinforcement Learning for Decision & Optimal Control - Springer

Deep Reinforcement Learning Hands-On: A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF

Deep Reinforcement Learning with Python: RLHF for Chatbots and Large Language Models

Reinforcement Learning and Dynamic Programming Using Function Approximators: 39 (Automation and Control Engineering)

Basic Mathematical Foundations of AI: Hands on with Python (Mastering Machine Learning)

Foundations of Reinforcement Learning with Applications in Finance (Chapman & Hall/CRC Mathematics and Artificial Intelligence Series)

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

Mathematical Foundations of Reinforcement Learning

Deep Reinforcement Learning: Fundamentals, Research and Applications

Reinforcement Learning – An Introduction

Deep Reinforcement Learning in Unity: With Unity ML Toolkit

Reinforcement Learning: Theory and Python Implementation

Multi-Agent Reinforcement Learning: Foundations and Modern Approaches

AlphaGo Simplified - Rule-Based AI and Deep Learning Book

Reinforcement Learning for Cyber Operations: Applications of Artificial Intelligence for Penetration Testing

Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications

Statistical Reinforcement Learning: Modern Machine Learning Approaches (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

AlphaGo Simplified: Rule-Based AI and Deep Learning in Everyday Games

Fundamentals of Machine Learning

Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools, and Applications

Machine Learning and Artificial Intelligence: Concepts, Algorithms and Models

Market Making Algorithms: 33 Comprehensive Powerful Algorithms With Full Python Code