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Deep Reinforcement Learning Hands-On
Deep Reinforcement Learning Hands-On

Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more , Second Edition

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Deep Reinforcement Learning Hands-On

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Key benefits

  • Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters
  • Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods
  • Apply RL methods to cheap hardware robotics platforms

Description

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.

Who is this book for?

Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL

What you will learn

  • Understand the deep learning context of RL and implement complex deep learning models
  • Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others
  • Build a practical hardware robot trained with RL methods for less than $100
  • Discover Microsoft s TextWorld environment, which is an interactive fiction games platform
  • Use discrete optimization in RL to solve a Rubik s Cube
  • Teach your agent to play Connect 4 using AlphaGo Zero
  • Explore the very latest deep RL research on topics including AI chatbots
  • Discover advanced exploration techniques, including noisy networks and network distillation techniques

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 31, 2020
Length: 826 pages
Edition : 2nd
Language : English
ISBN-13 : 9781838820046
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Languages :
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What do you get with eBook?

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Product Details

Publication date : Jan 31, 2020
Length: 826 pages
Edition : 2nd
Language : English
ISBN-13 : 9781838820046
Category :
Languages :
Tools :

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Table of Contents

27 Chapters
What Is Reinforcement Learning? Chevron down icon Chevron up icon
OpenAI Gym Chevron down icon Chevron up icon
Deep Learning with PyTorch Chevron down icon Chevron up icon
The Cross-Entropy Method Chevron down icon Chevron up icon
Tabular Learning and the Bellman Equation Chevron down icon Chevron up icon
Deep Q-Networks Chevron down icon Chevron up icon
Higher-Level RL Libraries Chevron down icon Chevron up icon
DQN Extensions Chevron down icon Chevron up icon
Ways to Speed up RL Chevron down icon Chevron up icon
Stocks Trading Using RL Chevron down icon Chevron up icon
Policy Gradients – an Alternative Chevron down icon Chevron up icon
The Actor-Critic Method Chevron down icon Chevron up icon
Asynchronous Advantage Actor-Critic Chevron down icon Chevron up icon
Training Chatbots with RL Chevron down icon Chevron up icon
The TextWorld Environment Chevron down icon Chevron up icon
Web Navigation Chevron down icon Chevron up icon
Continuous Action Space Chevron down icon Chevron up icon
RL in Robotics Chevron down icon Chevron up icon
Trust Regions – PPO, TRPO, ACKTR, and SAC Chevron down icon Chevron up icon
Black-Box Optimization in RL Chevron down icon Chevron up icon
Advanced Exploration Chevron down icon Chevron up icon
Beyond Model-Free – Imagination Chevron down icon Chevron up icon
AlphaGo Zero Chevron down icon Chevron up icon
RL in Discrete Optimization Chevron down icon Chevron up icon
Multi-agent RL Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(38 Ratings)
5 star 71.1%
4 star 10.5%
3 star 7.9%
2 star 0%
1 star 10.5%
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Machiel Kruger Feb 22, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Feefo Verified review Feefo
Oren Zeev-Ben-Mordehai Feb 24, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I enjoy the reading and I'm learning exactly what I was looking for and much more relevant material.
Feefo Verified review Feefo
vincent tanoe Mar 03, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I like the way the book is written and the explanation are well detailed !
Amazon Verified review Amazon
Keadtipoom Aug 25, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good book, read and run too easy.
Amazon Verified review Amazon
MrWiddles Jun 06, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As an IT person I had used supervised and unsupervised learning before but not RL. This book has good plain english engineering descriptions of the problem and solution as well as the maths. Rather than starting with descriptions of abstract models it shows how different challenges can be physically solved which is important in getting the first foothold of understanding the concepts of the game.I like the way it uses many illustrations and describes how a student could start using python libs to do testing. High res colour would have been nice but in practice not an issue for me.My personal interest is Smart Cities, Digital Twins and how RL can solve real world problems.
Amazon Verified review Amazon
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