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

Alessandro Palmas

Emanuele Ghelfi

Dr. Alexandra Galina Petre

Mayur Kulkarni

Anand N.S.

Quan Nguyen

Aritra Sen

Anthony So

Saikat Basak +5 more

$48.99

4.7 (7 Ratings)

Paperback Aug 2020 822 pages 1st Edition

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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.

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

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Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

The Natural Language Processing Workshop

$43.99

$48.99

$43.99

Total $ 136.97

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Amazon verified reviews

Marleen Jan 21, 2021

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

Juan C. Serrano (Data Scientist) Jan 12, 2021

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.

Sreerag Raghunathan Jan 18, 2021

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.

Manuel Montoya Jan 13, 2021

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.

lilfetz22 Jan 19, 2021

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.

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

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