The The Reinforcement Learning Workshop

Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems

; Emanuele Ghelfi ; Dr. Alexandra Galina Petre ; Mayur Kulkarni ; Anand N.S. ; Quan Nguyen ; Aritra Sen ; Anthony So ; Saikat Basak

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. Les mer
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Paperback
Legg i
Paperback
Legg i
Vår pris: 528,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

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, 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 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.

Fakta

Innholdsfortegnelse

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

Om forfatteren

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.