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Hands-On Reinforcement Learning with Python
Hands-On Reinforcement Learning with Python

Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

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Profile Icon Sudharsan Ravichandiran
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€18.99 per month
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.6 (18 Ratings)
Paperback Jun 2018 318 pages 1st Edition
eBook
€20.99 €23.99
Paperback
€29.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Sudharsan Ravichandiran
Arrow right icon
€18.99 per month
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.6 (18 Ratings)
Paperback Jun 2018 318 pages 1st Edition
eBook
€20.99 €23.99
Paperback
€29.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€20.99 €23.99
Paperback
€29.99
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Free Trial
Renews at €18.99p/m

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Hands-On Reinforcement Learning with Python

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Key benefits

  • •Your entry point into the world of artificial intelligence using the power of Python
  • •An example-rich guide to master various RL and DRL algorithms
  • •Explore various state-of-the-art architectures along with math

Description

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.

Who is this book for?

If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.

What you will learn

  • Understand the basics of reinforcement learning methods, algorithms, and elements
  • Train an agent to walk using OpenAI Gym and Tensorflow
  • Understand the Markov Decision Process, Bellman's optimality, and TD learning
  • Solve multi-armed-bandit problems using various algorithms
  • Master deep learning algorithms, such as RNN, LSTM, and CNN with applications
  • Build intelligent agents using the DRQN algorithm to play the Doom game
  • Teach agents to play the Lunar Lander game using DDPG
  • Train an agent to win a car racing game using dueling DQN

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 28, 2018
Length: 318 pages
Edition : 1st
Language : English
ISBN-13 : 9781788836524
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Product Details

Publication date : Jun 28, 2018
Length: 318 pages
Edition : 1st
Language : English
ISBN-13 : 9781788836524
Category :
Languages :

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Table of Contents

15 Chapters
Introduction to Reinforcement Learning Chevron down icon Chevron up icon
Getting Started with OpenAI and TensorFlow Chevron down icon Chevron up icon
The Markov Decision Process and Dynamic Programming Chevron down icon Chevron up icon
Gaming with Monte Carlo Methods Chevron down icon Chevron up icon
Temporal Difference Learning Chevron down icon Chevron up icon
Multi-Armed Bandit Problem Chevron down icon Chevron up icon
Deep Learning Fundamentals Chevron down icon Chevron up icon
Atari Games with Deep Q Network Chevron down icon Chevron up icon
Playing Doom with a Deep Recurrent Q Network Chevron down icon Chevron up icon
The Asynchronous Advantage Actor Critic Network Chevron down icon Chevron up icon
Policy Gradients and Optimization Chevron down icon Chevron up icon
Capstone Project – Car Racing Using DQN Chevron down icon Chevron up icon
Recent Advancements and Next Steps Chevron down icon Chevron up icon
Assessments Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.6
(18 Ratings)
5 star 22.2%
4 star 5.6%
3 star 22.2%
2 star 11.1%
1 star 38.9%
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Top Reviews

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Antonio Gulli Aug 17, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
reinforcement learning made simple. Simple solid math when needed, with good python code.Solid introduction to reinforcement learning traditional strategies and modern deep reinforcement learning.Definitively recommend.
Amazon Verified review Amazon
Sam mus Aug 16, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book. Extremely useful. I got the chance to understand how to use RL with clear examples and a solid mathematical background that is suitably explained for Machine Learning practitioners
Amazon Verified review Amazon
Arivarasan.E Dec 29, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book starts with building strong foundations to Reinforcement Learning and then explains deep reinforcement learning algorithms. I really liked the way author has explained advanced concepts in such a simple and more intuitive way. Also, I never know I can understand math so simply. Building Applications like training robot to walk, building car racing agent, lunar lander really makes it fun while learning. Overall awesome book.
Amazon Verified review Amazon
Sam Jul 12, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have not gone through all the chapters yet but this looks promising for detailed knowledge. Great to have this book.
Amazon Verified review Amazon
Mark Oct 30, 2019
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Very good examples and plain delivery of knowledge. Great for beginners.
Amazon Verified review Amazon
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