Deep reinforcement learning hands-on : a practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF / Maxim Lapan.

Format:

Book

Author/Creator:

Lapan, Maxim, author.

Series:

Expert insight.

Expert insight

Language:

English

Subjects (All):

Reinforcement learning.

Machine learning.

Natural language processing (Computer science).

Artificial intelligence.

Physical Description:

1 online resource (716 pages) : illustrations

Edition:

Third edition.

Place of Publication:

Birmingham, UK : Packt Publishing Ltd., 2024.

Summary:

Start your journey into reinforcement learning (RL) and reward yourself with the third edition of Deep Reinforcement Learning Hands-On. This book takes you through the basics of RL to more advanced concepts with the help of various applications, including game playing, discrete optimization, stock trading, and web browser navigation. By walking you through landmark research papers in the fi eld, this deep RL book will equip you with practical knowledge of RL and the theoretical foundation to understand and implement most modern RL papers. The book retains its approach of providing concise and easy-to-follow explanations from the previous editions. You'll work through practical and diverse examples, from grid environments and games to stock trading and RL agents in web environments, to give you a well-rounded understanding of RL, its capabilities, and its use cases. You'll learn about key topics, such as deep Q-networks (DQNs), policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. If you want to learn about RL through a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition, is your ideal companion.

Contents:

Cover

Copyright Page

Contributors

Table of Contents

Preface

PART I : INTRODUCTION TO RL

Chapter 1: What Is Reinforcement Learning?

Chapter 2: OpenAI Gym API and Gymnasium

Chapter 3: Deep Learning with PyTorch

Chapter 4: The Cross-Entropy Method

PART I I: VALUE-BASED METHODS

Chapter 5: Tabular Learning and the Bellman Equation

Chapter 6: Deep Q-Networks

Chapter 7: Higher-Level RL Libraries

Chapter 8: DQN Extensions

Chapter 9: Ways to Speed Up RL

Chapter 10: Stocks Trading Using RL

PART I I I: POLICY-BASED METHODS

Chapter 11: Policy Gradients

Chapter 12: Actor-Critic Method: A2C and A3C

Chapter 13: The TextWorld Environment

Chapter 14: Web Navigation

PART IV: ADVANCED RL

Chapter 15: Continous Action Space

Chapter 16: Trust Region Methods

Chapter 17: Black-Box Optimizations in RL

Chapter 18: Advanced Exploration

Chapter 19: Reinforcement Learning with Human Feedback

Chapter 20: AlphaGo Zero and MuZero

Chapter 21: RL in Discrete Optimization

Chapter 22: Multi-Agent RL

Bibliography

Packt Page

Other Books You May Enjoy

Index.

Notes:

OCLC-licensed vendor bibliographic record.

Description based on publisher supplied metadata and other sources.

ISBN:

9781835882719

1835882714

9781835882702

1835882706

OCLC:

1472090174