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

₱1799.99 ~~₱2000.99~~

4.7 (7 Ratings)

eBook Aug 2020 822 pages 1st Edition

eBook

₱1799.99 ~~₱2000.99~~

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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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

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

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About the 9 authors

Alessandro Palmas

Alessandro Palmas is an aerospace engineer with more than 7 years of proven expertise in software development for advanced scientific applications and complex software systems. As the R&D head in an aerospace & defense Italian SME, he coordinates projects in contexts ranging from space flight dynamics to machine learning-based autonomous systems. His main ML focus is on computer vision, 3D models, volumetric networks, and deep reinforcement learning. He also founded innovative initiatives, his last being Artificial Twin, which provides advanced technologies for machine learning, physical modeling, and computational geometry applications. Two key areas in which current Artificial Twin deep RL work is focused on are video games entertainment, and guidance, navigation & control systems.

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Emanuele Ghelfi

Emanuele Ghelfi is a computer science and machine learning engineer. He received an M.Sc. degree in computer science and engineering at Politecnico di Milano in December 2018. In his thesis, he proposed a new RL algorithm for an MDP extension. The paper from the thesis got accepted at ICML 2019. Hes an organizer of the community data science and artificial intelligence in Parma. Emanuele presented tutorials about generative adversarial networks at conferences like PyCon X (Florence) and EuroSciPy (Bilbao). He is also a developer of the machine learning package AshPy, available on GitHub and PyPi.

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Dr. Alexandra Galina Petre

Dr. Alexandra Galina Petre is a machine learning and data science expert, currently leading and teaching various engineering modules in Coventry, United Kingdom. Her leadership and management experience is linked to her work in quality management for the Airbus A380 and her IET membership. She received her Ph.D. in user feedback-based reinforcement learning for vehicle comfort control with a focus on revolutionary heating ventilation and air conditioning SARSA-based control systems that can learn from the drivers preferential changes to the UI. Her research is focusing on how thermal comfort depends on the occupants inclination to manual control as outlined in the SAE paper published in 2019, and the development of a novel Java-based user model (UBL) integrated within a car cabin environment. She is working on deep RL implementations in Python and R-based statistical developments within various automation and control projects.

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Mayur Kulkarni

Mayur Kulkarni works in the Machine Learning research team at Microsoft and has previously been at IIT Bombay, and IIM Lucknow. He has also been an instructor for the postgraduate programs in Artificial Intelligence and Machine Learning at UpGrad and IIIT Bangalore, covering topics in Deep Reinforcement Learning. He is one of the contributors to DVC, torch, and scikit-learn, which are some of the most popular open-source machine learning libraries in Python.

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Anand N.S.

Anand N.S. has more than two decades of technology experience working, with a strong hands-on track record of application of artificial intelligence, machine learning, and data science to create measurable business outcomes. He has been granted several US patents in the areas of data science, machine learning, and artificial Intelligence. Anand has a B.Tech in Electrical Engineering from IIT Madras and an MBA with a Gold Medal from IIM Kozhikode.

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Quan Nguyen

Quan Nguyen, the author of the first edition of this book, is a Python programmer with a strong passion for machine learning. He holds a dual degree in mathematics and computer science, with a minor in philosophy, earned from DePauw University. Quan is deeply involved in the Python community and has authored multiple Python books, contributing to the Python Software Foundation and regularly sharing insights on DataScience portal. He is currently pursuing a Ph.D. in computer science at Washington University in St. Louis.

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Aritra Sen

Aritra Sen currently works as a data scientist in Ericsson. His current role includes building and deploying large scale machine learning solutions for the telecom industry. He has around 10 years of experience in data science and business intelligence. He previously worked in Cognizant, KPMG, IBM, and TCS. Aritra also has a keen interest in blogging and he regularly writes about machine learning, deep learning, etc. He also filed a patent related to the telecom industry.

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Anthony So

Anthony So is a renowned leader in data science. He has extensive experience in solving complex business problems using advanced analytics and AI in different industries including financial services, media, and telecommunications. He is currently the chief data officer of one of the most innovative fintech start-ups. He is also the author of several best-selling books on data science, machine learning, and deep learning. He has won multiple prizes at several hackathon competitions, such as Unearthed, GovHack, and Pepper Money. Anthony holds two master's degrees, one in computer science and the other in data science and innovation.

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Saikat Basak

Saikat Basak is a data scientist and a passionate programmer. Having worked with multiple industry leaders, he has a good understanding of problem areas that can potentially be solved using data. Apart from being a data guy, he is also a science geek and loves to explore new ideas in the frontiers of science and technology.

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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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