The The Reinforcement Learning Workshop

The The Reinforcement Learning Workshop
Author: Alessandro Palmas
Publisher: Packt Publishing Ltd
Total Pages: 821
Release: 2020-08-18
Genre: Computers
ISBN: 1800209967


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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 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 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, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll 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, you’ll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you’ll 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 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.


The The Reinforcement Learning Workshop
Language: en
Pages: 821
Authors: Alessandro Palmas
Categories: Computers
Type: BOOK - Published: 2020-08-18 - Publisher: Packt Publishing Ltd

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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
The The Machine Learning Workshop
Language: en
Pages: 285
Authors: Hyatt Saleh
Categories: Computers
Type: BOOK - Published: 2020-07-22 - Publisher: Packt Publishing Ltd

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Take a comprehensive and step-by-step approach to understanding machine learning Key FeaturesDiscover how to apply the scikit-learn uniform API in all types of
Reinforcement Learning, second edition
Language: en
Pages: 549
Authors: Richard S. Sutton
Categories: Computers
Type: BOOK - Published: 2018-11-13 - Publisher: MIT Press

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The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intellig
Optimization for Machine Learning
Language: en
Pages: 509
Authors: Suvrit Sra
Categories: Computers
Type: BOOK - Published: 2012 - Publisher: MIT Press

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An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay betw
Deep Reinforcement Learning in Action
Language: en
Pages: 381
Authors: Alexander Zai
Categories: Computers
Type: BOOK - Published: 2020-04-28 - Publisher: Manning Publications

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Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences