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The Reinforcement Learning Workshop
The Reinforcement Learning Workshop

The Reinforcement Learning Workshop: Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems

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Profile Icon Alessandro Palmas Profile Icon Emanuele Ghelfi Profile Icon Dr. Alexandra Galina Petre Profile Icon Mayur Kulkarni Profile Icon Anand N.S. Profile Icon Quan Nguyen Profile Icon Aritra Sen Profile Icon Anthony So Profile Icon Saikat Basak +5 more Show less
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₱1799.99 ₱2000.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (7 Ratings)
eBook Aug 2020 822 pages 1st Edition
eBook
₱1799.99 ₱2000.99
Paperback
₱2500.99
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Arrow left icon
Profile Icon Alessandro Palmas Profile Icon Emanuele Ghelfi Profile Icon Dr. Alexandra Galina Petre Profile Icon Mayur Kulkarni Profile Icon Anand N.S. Profile Icon Quan Nguyen Profile Icon Aritra Sen Profile Icon Anthony So Profile Icon Saikat Basak +5 more Show less
Arrow right icon
₱1799.99 ₱2000.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (7 Ratings)
eBook Aug 2020 822 pages 1st Edition
eBook
₱1799.99 ₱2000.99
Paperback
₱2500.99
Subscription
Free Trial
eBook
₱1799.99 ₱2000.99
Paperback
₱2500.99
Subscription
Free Trial

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The Reinforcement Learning Workshop

2. Markov Decision Processes and Bellman Equations

Overview

This chapter will cover more of the theory behind reinforcement learning. We will cover Markov chains, Markov reward processes, and Markov decision processes. We will learn about the concepts of state values and action values along with Bellman equations to calculate previous quantities. By the end of this chapter, you will be able to solve Markov decision processes using linear programming methods.

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Key benefits

  • Use TensorFlow to write reinforcement learning agents for performing challenging tasks
  • Learn how to solve finite Markov decision problems
  • Train models to understand popular video games like Breakout

Description

Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, youÔÇÖll be guided through different RL environments and frameworks. YouÔÇÖll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once youÔÇÖve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, youÔÇÖll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, youÔÇÖll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, youÔÇÖll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.

Who is this book for?

If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.

What you will learn

  • Use OpenAI Gym as a framework to implement RL environments
  • Find out how to define and implement reward function
  • Explore Markov chain, Markov decision process, and the Bellman equation
  • Distinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning
  • Understand the multi-armed bandit problem and explore various strategies to solve it
  • Build a deep Q model network for playing the video game Breakout

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 18, 2020
Length: 822 pages
Edition : 1st
Language : English
ISBN-13 : 9781800209961
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Google
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Product Details

Publication date : Aug 18, 2020
Length: 822 pages
Edition : 1st
Language : English
ISBN-13 : 9781800209961
Vendor :
Google
Category :
Languages :

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Frequently bought together


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The Reinforcement Learning Workshop
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The Deep Learning Workshop
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Table of Contents

12 Chapters
1. Introduction to Reinforcement Learning Chevron down icon Chevron up icon
2. Markov Decision Processes and Bellman Equations Chevron down icon Chevron up icon
3. Deep Learning in Practice with TensorFlow 2 Chevron down icon Chevron up icon
4. Getting Started with OpenAI and TensorFlow for Reinforcement Learning Chevron down icon Chevron up icon
5. Dynamic Programming Chevron down icon Chevron up icon
6. Monte Carlo Methods Chevron down icon Chevron up icon
7. Temporal Difference Learning Chevron down icon Chevron up icon
8. The Multi-Armed Bandit Problem Chevron down icon Chevron up icon
9. What Is Deep Q-Learning? Chevron down icon Chevron up icon
10. Playing an Atari Game with Deep Recurrent Q-Networks Chevron down icon Chevron up icon
11. Policy-Based Methods for Reinforcement Learning Chevron down icon Chevron up icon
12. Evolutionary Strategies for RL Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7
(7 Ratings)
5 star 71.4%
4 star 28.6%
3 star 0%
2 star 0%
1 star 0%
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Marleen Jan 21, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
After taking a university class in Reinforcement Learning I was looking for a book to complement my new knowledge. This workshop serves the purpose better than I expected. I found a summary of all topics that we had discussed prior in class, for example the main building blocks: action, environment, agent and policy but also Markov chains, the famous Bellman equation, Deep-Q learning and the Actor-Critic model. To my surprise this book also covered so many more topics, if you take your time to really understand each chapter and follow along the practice exercises you will most definitely have an extensive understanding of RL afterwards. The exercises are similar to the ones I got to know in class so I guess those are common RL problems. Other than that I also liked that Open AI is being discussed. To sum up, if you are new to the subject or don't mind exercises you might already know I would definitely recommend this workshop to you.
Amazon Verified review Amazon
Juan C. Serrano (Data Scientist) Jan 12, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is a very comprehensive and detailed review on Reinforcement Learning. The authors have put a lot of effort to make the book easy to read for a mid to advanced Data Scientist. For instance, the book contains an extensive guide on how to install libraries, and dependencies that will be used in subsequent chapters. The authors refer to many real-life examples that help the reader grasp the theory and serve as motivation to learn more about the subject. The notation and language of the book are easy to follow. The chapters introduce complex concepts in a step-by-step fashion, leveraging on diagrams and code examples as well as real life applications when required which in my opinion is the best way to learn Data Science. Albeit the code boxes are difficult to follow due to splitting the code to fit into one page; most of the code exercises have a Github backup and that makes them easy to replicate.On the downside, I was also not able to work on OPENAI databases examples as recent Python updates make it very difficult to install the required libraries.
Amazon Verified review Amazon
Sreerag Raghunathan Jan 18, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is a great introduction and also a hands on approach to Reinforced Learning. I believe both RL enthusiasts and students would benefit from it alike. It builds a good foundation in the subject without too much complex mathematics and so focusing more on the applied part.Supplementing with working code is another great plus for this book.
Amazon Verified review Amazon
Manuel Montoya Jan 13, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It’s a throughout guide on reinforcement learning and its applications. The concepts are well organized, starting with the basic concepts of learning paradigms, agents, and policies to more advanced subjects like Q learning and contextual bandits. I recommend it to anyone with some experience with machine learning and wants to expand their knowledge of reinforcement learning. It sure has helped me to understand some new concepts and how to apply them.
Amazon Verified review Amazon
lilfetz22 Jan 19, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
If you are ready to commit some serious time to diving deep into Reinforcement Learning then this is the book for you. This book is laid out as a workshop where it not only explains everything about Reinforcement learning, but also gives you perfect examples to work out as you are going through. This is key as every programmer knows that you don't truly understand something until you go and do it for yourself. This book provides exactly that. I loved being able to work out example problems as I was also learning the theory behind each of the models and the reasons why I might choose one model over another. The authors go through everything in great detail, and they provide you with everything you need to know about Reinforcement Learning.I would highly recommend this book to any data scientist, machine learning engineer, or software engineer that is looking to up their skills an knowledge on Reinforcement Learning.
Amazon Verified review Amazon
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