Regular price €44.99
A01=Alessandro Palmas
A01=Anand N.S.
A01=Anthony So
A01=Aritra Sen
A01=Dr. Alexandra Galina Petre
A01=Emanuele Ghelfi
A01=Mayur Kulkarni
A01=Quan Nguyen
A01=Saikat Basak
Author_Alessandro Palmas
Author_Anand N.S.
Author_Anthony So
Author_Aritra Sen
Author_Dr. Alexandra Galina Petre
Author_Emanuele Ghelfi
Author_Mayur Kulkarni
Author_Quan Nguyen
Author_Saikat Basak
Breakout
Category=UMX
Category=UYQN
deep learning
Deep reinforcement learning
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
machine learning
Markov chain Monte Carlo
markov decision process
Monte Carlo method
Monte Carlo model
Python

Product details

  • ISBN 9781800200456
  • Dimensions: 75 x 93mm
  • Publication Date: 18 Aug 2020
  • Publisher: Packt Publishing Limited
  • Publication City/Country: GB
  • Product Form: Paperback
Delivery/Collection within 10-20 working days

Our Delivery Time Frames Explained
2-4 Working Days: Available in-stock

10-20 Working Days: On Backorder

Will Deliver When Available: On Pre-Order or Reprinting

We ship your order once all items have arrived at our warehouse and are processed. Need those 2-4 day shipping items sooner? Just place a separate order for them!

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 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 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 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.

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.