Search icon Close icon
Search icon
Subscription
0
Cart icon
Close icon
You have no products in your basket yet
Save more on your purchases!
Savings automatically calculated. No voucher code required
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Mastering Reinforcement Learning with Python

You're reading from  Mastering Reinforcement Learning with Python

Product type Book
Published in Dec 2020
Publisher Packt
ISBN-13 9781838644147
Pages 544 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Enes Bilgin Enes Bilgin
Profile icon Enes Bilgin
LinkedIn
Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.
See other products by Enes Bilgin

Table of Contents (24) Chapters

Preface 1. Section 1: Reinforcement Learning Foundations
2. Chapter 1: Introduction to Reinforcement Learning 3. Chapter 2: Multi-Armed Bandits 4. Chapter 3: Contextual Bandits 5. Chapter 4: Makings of a Markov Decision Process 6. Chapter 5: Solving the Reinforcement Learning Problem 7. Section 2: Deep Reinforcement Learning
8. Chapter 6: Deep Q-Learning at Scale 9. Chapter 7: Policy-Based Methods 10. Chapter 8: Model-Based Methods 11. Chapter 9: Multi-Agent Reinforcement Learning 12. Section 3: Advanced Topics in RL
13. Chapter 10: Introducing Machine Teaching 14. Chapter 11: Achieving Generalization and Overcoming Partial Observability 15. Chapter 12: Meta-Reinforcement Learning 16. Chapter 13: Exploring Advanced Topics 17. Section 4: Applications of RL
18. Chapter 14: Solving Robot Learning 19. Chapter 15: Supply Chain Management 20. Chapter 16: Personalization, Marketing, and Finance 21. Chapter 17: Smart City and Cybersecurity 22. Chapter 18: Challenges and Future Directions in Reinforcement Learning 23. Other Books You May Enjoy

Chapter 1: Introduction to Reinforcement Learning

Reinforcement Learning (RL) aims to create Artificial Intelligence (AI) agents that can make decisions in complex and uncertain environments, with the goal of maximizing their long-term benefit. These agents learn how to do it through interacting with their environments, which mimics the way we as humans learn from experience. As such, RL has an incredibly broad and adaptable set of applications, with the potential to disrupt and revolutionize global industries.

This book will give you an advanced level understanding of this field. We will go deeper into the theory behind the algorithms you may already know, and cover state-of-the art RL. Moreover, this is a practical book. You will see examples inspired by real-world industry problems and learn expert tips along the way. By its conclusion, you will be able to model and solve your own sequential decision-making problems using Python.

So, let's start our journey with refreshing your mind on RL concepts and get you set up for the advanced material upcoming in the following chapters. Specifically, this chapter covers:

  • Why reinforcement learning?
  • The three paradigms of ML
  • RL application areas and success stories
  • Elements of a RL problem
  • Setting up your RL environment
arrow left Previous Chapter
Chapter 1 of 24
Next Chapter arrow right
You have been reading a chapter from
Mastering Reinforcement Learning with Python
Published in: Dec 2020 Publisher: Packt ISBN-13: 9781838644147
© 2020 Packt Publishing Limited All Rights Reserved
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Sign up now
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Start free trial
Renews at $15.99/month. Cancel anytime}

Authors (1)

Profile icon Enes Bilgin
LinkedIn
Enes Bilgin works as a senior AI engineer and a tech lead in Microsoft's Autonomous Systems division. He is a machine learning and operations research practitioner and researcher with experience in building production systems and models for top tech companies using Python, TensorFlow, and Ray/RLlib. He holds an M.S. and a Ph.D. in systems engineering from Boston University and a B.S. in industrial engineering from Bilkent University. In the past, he has worked as a research scientist at Amazon and as an operations research scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.
Read more
See other products by Enes Bilgin

Other recommended products

Related to this chapter
Left arrow icon
TensorFlow Reinforcement Learning Quick Start Guide
TensorFlow Reinforcement Learning Quick Start Guide
This book is an essential guide for anyone interested in Reinforcement Learning. The book provides an actionable reference for Reinforcement Learning algorithms and their applications using TensorFlow and Python. It will help readers leverage the power of algorithms such as Deep Q-Network (DQN), Deep Deterministic Policy Gradients (DDPG), and Proximal Policy Optimization (PPO) to solve challenging control and decision-making problems.
Mar 2019 6 hours 8 minutes
TensorFlow 2 Reinforcement Learning Cookbook
TensorFlow 2 Reinforcement Learning Cookbook
This cookbook will help you to gain a solid understanding of deep reinforcement learning (RL) algorithms with the help of concise, easy-to-follow implementations from scratch. You'll learn how to implement these algorithms with minimal code and develop AI applications to solve real-world and business problems using RL.
Jan 2021 15 hours 44 minutes
PyTorch 1.x Reinforcement Learning Cookbook
PyTorch 1.x Reinforcement Learning Cookbook
This book presents practical solutions to the most common reinforcement learning problems. The recipes in this book will help you understand the fundamental concepts to develop popular RL algorithms. You will gain practical experience in the RL domain using the modern offerings of the PyTorch 1.x library.
Oct 2019 11 hours 20 minutes
Deep Reinforcement Learning with Python
Deep Reinforcement Learning with Python
Deep Reinforcement Learning with Python - Second Edition will help you learn reinforcement learning algorithms, techniques and architectures – including deep reinforcement learning – from scratch. This new edition is an extensive update of the original, reflecting the state-of-the-art latest thinking in reinforcement learning.
Sep 2020 25 hours 20 minutes
Hands-On Reinforcement Learning with Python
Hands-On Reinforcement Learning with Python
Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python.
Jun 2018 10 hours 36 minutes
Reinforcement Learning Algorithms with Python
Reinforcement Learning Algorithms with Python
With this book, you will understand the core concepts and techniques of reinforcement learning. You will take a look into each RL algorithm and will develop your own self-learning algorithms and models. You will optimize the algorithms for better precision, use high-speed actions and lower the risk of anomalies in your applications.
Oct 2019 12 hours 12 minutes
Hands-On Intelligent Agents with OpenAI Gym
Hands-On Intelligent Agents with OpenAI Gym
Walks through the hands-on process of building intelligent agents from the basics and all the way up to solving complex problems including playing Atari games and driving a car autonomously in the CARLA simulator. Discusses various learning environments and how to transform real-world problems into learning environments and solve using the agents.
Jul 2018 8 hours 28 minutes
Reinforcement Learning with TensorFlow
Reinforcement Learning with TensorFlow
Reinforcement learning allows you to develop intelligent, self-learning systems. This book shows you how to put the concepts of Reinforcement Learning to train efficient models.You will use popular reinforcement learning algorithms to implement use-cases in image processing and NLP, by combining the power of TensorFlow and OpenAI Gym.
Apr 2018 11 hours 8 minutes
Hands-On Q-Learning with Python
Hands-On Q-Learning with Python
Q-learning is the reinforcement learning approach behind Deep-Q-Learning and is a values-based learning algorithm in RL. This book will help you get comfortable with developing the effective agents for Q learning and also make you learn to effectively develop and deploy Deep Q networks for complex AI applications.
Apr 2019 7 hours 4 minutes
The Reinforcement Learning Workshop
The Reinforcement Learning Workshop
With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning’s core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intelligent applications with ease.
Aug 2020 27 hours 24 minutes
Hands-On Markov Models with Python
Hands-On Markov Models with Python
This book will help you become familiar with HMMs and different inference algorithms by working on real-world problems. You will start with an introduction to the basic concepts of Markov chains, Markov processes and then delve deeper into understanding hidden Markov models and its types using practical examples.
Sep 2018 5 hours 56 minutes
Practical Reinforcement Learning
Practical Reinforcement Learning
Reinforcement learning (RL) is becoming a popular tool for constructing autonomous systems that improve themselves with experience. We will break the RL framework into its core building blocks, and provide you with details of each element. This book is divided into three parts. The first part defines Reinforcement Learning and describes the basics and the Python and Java frameworks, which we are going to use later in the book. The second part discusses learning techniques with basic algorithms such as Temporal Difference, Monte Carlo, and Policy Gradient—all with practical examples. Lastly, in the third part we apply Reinforcement Learning with the most recent and widely used algorithms, via practical applications.
Oct 2017 11 hours 12 minutes
Right arrow icon

Personalised recommendations for you

Based on your interests and search pattern
Left arrow icon
Et al.
Et al.
Ever wonder why speech recognition systems don't understand the Scottish accent, or what would happen if an astronaut only ate mac 'n' cheese, or other spurious reflections you'd have at a bar? We did, then collated those deliberations into absurd research articles with fake figures and methodologies inspired by even more fictionally absurd studies.
Aug 2023 7 hours 40 minutes
Generative AI with LangChain
Generative AI with LangChain
This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.
Dec 2023 12 hours 0 minutes
Generative AI with LangChain
Generative AI with LangChain
This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.
Dec 2023 12 hours 0 minutes
Generative AI with LangChain
Generative AI with LangChain
This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.
Dec 2023 12 hours 0 minutes
Generative AI with LangChain
Generative AI with LangChain
This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.
Dec 2023 12 hours 0 minutes
Mastering Tableau 2023
Mastering Tableau 2023
This book is a comprehensive resource to mastering your Tableau skills and becoming a BI expert. As you progress, you will learn how to build advanced dashboards and improve your storytelling to derive key business insight, as well as make you well-versed with advanced functionalities of Tableau in the business intelligence domain.
Aug 2023 22 hours 48 minutes
Building AI Applications with ChatGPT APIs
Building AI Applications with ChatGPT APIs
This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.
Sep 2023 8 hours 36 minutes
Building AI Applications with ChatGPT APIs
Building AI Applications with ChatGPT APIs
This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.
Sep 2023 8 hours 36 minutes
Data Engineering with AWS
Data Engineering with AWS
Embark on a journey to master data engineering pipelines on AWS! Our book offers a hands-on experience of AWS services for ingesting, transforming, and consuming data. Whether you're an absolute beginner or someone with basic data engineering experience, this guide is an indispensable resource.
Oct 2023 21 hours 12 minutes
Modern Data Architecture on AWS
Modern Data Architecture on AWS
Every organization wants an agile, performant, and cost-effective data platform that meets all their current and future business needs. Purpose-built AWS analytics services and their features play a big part in building such a modern data platform. This book brings to you all the design and architectural patterns that’ll help you achieve this goal.
Aug 2023 14 hours 0 minutes
Practical Guide to Applied Conformal Prediction in Python
Practical Guide to Applied Conformal Prediction in Python
Discover the power of Conformal Prediction with the "Practical Guide to Applied Conformal Prediction in Python." Master the latest techniques to quantify uncertainty in machine learning and computer vision models, and seamlessly apply them to your industry applications.
Dec 2023 8 hours 0 minutes
TinyML Cookbook
TinyML Cookbook
With over 70 project-based recipes, the TinyML Cookbook is a practical guide that will help you to get the most out of your microcontrollers. It provides a comprehensive understanding of the theoretical foundations while giving you hands-on experience training ML models for deployment on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano microcontrollers.
Nov 2023 22 hours 8 minutes
Right arrow icon