The Reinforcement Learning Workshop
Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems
Published on 18. August 2020
822 pages
978-1-80020-996-1 (ISBN)
Description
Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide
Key Features
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
Book DescriptionVarious 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, youOCOEll be guided through different RL environments and frameworks. YouOCOEll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once youOCOEve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, youOCOEll 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, youOCOEll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, youOCOEll 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
Who this book is forIf 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.
Key Features
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
Book DescriptionVarious 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, youOCOEll be guided through different RL environments and frameworks. YouOCOEll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once youOCOEve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, youOCOEll 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, youOCOEll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, youOCOEll 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
Who this book is forIf 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.
More details
Edition
Digital original
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Digital original
File size
23,37 MB
ISBN-13
978-1-80020-996-1 (9781800209961)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Alessandro Palmas | Emanuele Ghelfi | Dr. Alexandra Galina Petre
The The Reinforcement Learning Workshop
Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems
Book
08/2020
Packt Publishing
€60.30
Shipment within 3-4 weeks
Persons
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. 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. 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. 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. 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. 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. 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. 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. 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. Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP) and behavioral analysis to machine learning and distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events. Contacted author for "The AI Infratsructure Workshop" on 09/03/20 by Anindya Sil
Content
Table of Contents
Introduction to Reinforcement Learning
Markov Decision Processes and Bellman Equations
Deep Learning in Practice with TensorFlow 2
Getting Started with OpenAI and TensorFlow for Reinforcement Learning
Dynamic Programming
Monte Carlo Methods
Temporal Difference Learning
The Multi-Armed Bandit Problem
What Is Deep Q Learning?
Playing an Atari Game with Deep Recurrent Q Networks
Policy-Based Methods for Reinforcement Learning
Evolutionary Strategies for RL
Introduction to Reinforcement Learning
Markov Decision Processes and Bellman Equations
Deep Learning in Practice with TensorFlow 2
Getting Started with OpenAI and TensorFlow for Reinforcement Learning
Dynamic Programming
Monte Carlo Methods
Temporal Difference Learning
The Multi-Armed Bandit Problem
What Is Deep Q Learning?
Playing an Atari Game with Deep Recurrent Q Networks
Policy-Based Methods for Reinforcement Learning
Evolutionary Strategies for RL
