The The Reinforcement Learning Workshop: Learn how to apply cutting-edge reinfor

Description: The The Reinforcement Learning Workshop by Alessandro Palmas, Emanuele Ghelfi, Dr. Alexandra Galina Petre, Mayur Kulkarni, Anand N.S., Quan Nguyen, Aritra Sen, Anthony So, Saikat Basak With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learnings 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. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher 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 guideKey FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook 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, youll be guided through different RL environments and frameworks. Youll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once youve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, youll 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, youll find out when to use a policy-based method to tackle an RL problem.By the end of The Reinforcement Learning Workshop, youll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho 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. Author Biography Alessandro Palmas has more than 7 years of proven expertise in software development for advanced scientific applications and complex software systems. Emanuele Ghelfi is a computer science and machine learning engineer. Dr. Alexandra Galina Petre is a machine learning and data science expert. Mayur Kulkarni works in the machine learning research team at Microsoft. Anand N.S. has a strong hands-on track record of working on applications for artificial intelligence. Quan Nguyen is a programmer with a special interest in artificial intelligence. Aritra Sen currently works as a data scientist in Ericsson. Anthony So is an outstanding leader with more than 13 years of experience. Saikat Basak is a data scientist and a passionate programmer. Table of Contents Table of ContentsIntroduction to Reinforcement LearningMarkov Decision Processes and Bellman EquationsDeep Learning in Practice with TensorFlow 2Getting Started with OpenAI and TensorFlow for Reinforcement LearningDynamic ProgrammingMonte Carlo MethodsTemporal Difference LearningThe Multi-Armed Bandit ProblemWhat Is Deep Q Learning?Playing an Atari Game with Deep Recurrent Q NetworksPolicy-Based Methods for Reinforcement LearningEvolutionary Strategies for RL Details ISBN1800200455 Author Saikat Basak Short Title The Reinforcement Learning Workshop Pages 822 Language English Year 2020 ISBN-10 1800200455 ISBN-13 9781800200456 Format Paperback Publication Date 2020-08-18 Publisher Packt Publishing Limited UK Release Date 2020-08-18 Imprint Packt Publishing Limited Place of Publication Birmingham Country of Publication United Kingdom AU Release Date 2020-08-18 NZ Release Date 2020-08-18 Subtitle Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems DEWEY 005.7 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:129626992;

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ISBN-13: 9781800200456

Book Title: The The Reinforcement Learning Workshop

Publisher: Packt Publishing Limited

Publication Year: 2020

Subject: Computer Science

Item Height: 93 mm

Number of Pages: 822 Pages

Language: English

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

Type: Textbook

Author: Anthony So, Quan Nguyen, Aritra Sen, Saikat Basak, Dr. Alexandra Galina Petre, Anand N.S., Emanuele Ghelfi, Mayur Kulkarni, Alessandro Palmas

Item Width: 75 mm

Format: Paperback