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Deep Reinforcement Learning with Python
Deep Reinforcement Learning with Python

Deep Reinforcement Learning with Python: Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow , Second Edition

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Profile Icon Sudharsan Ravichandiran
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$35.98 $39.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (20 Ratings)
eBook Sep 2020 760 pages 2nd Edition
eBook
$35.98 $39.99
Paperback
$48.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Sudharsan Ravichandiran
Arrow right icon
$35.98 $39.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (20 Ratings)
eBook Sep 2020 760 pages 2nd Edition
eBook
$35.98 $39.99
Paperback
$48.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$35.98 $39.99
Paperback
$48.99
Subscription
Free Trial
Renews at $19.99p/m

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Deep Reinforcement Learning with Python

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Key benefits

  • Covers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithm
  • Learn how to implement algorithms with code by following examples with line-by-line explanations
  • Explore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrations

Description

With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.

Who is this book for?

If you’re a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.

What you will learn

  • Understand core RL concepts including the methodologies, math, and code
  • Train an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI Gym
  • Train an agent to play Ms Pac-Man using a Deep Q Network
  • Learn policy-based, value-based, and actor-critic methods
  • Master the math behind DDPG, TD3, TRPO, PPO, and many others
  • Explore new avenues such as the distributional RL, meta RL, and inverse RL
  • Use Stable Baselines to train an agent to walk and play Atari games

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Sep 30, 2020
Length: 760 pages
Edition : 2nd
Language : English
ISBN-13 : 9781839215599
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Product Details

Publication date : Sep 30, 2020
Length: 760 pages
Edition : 2nd
Language : English
ISBN-13 : 9781839215599
Category :

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Frequently bought together


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Deep Reinforcement Learning with Python
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Mastering Reinforcement Learning with Python
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Total $ 177.97 Stars icon

Table of Contents

19 Chapters
Fundamentals of Reinforcement Learning Chevron down icon Chevron up icon
A Guide to the Gym Toolkit Chevron down icon Chevron up icon
The Bellman Equation and Dynamic Programming Chevron down icon Chevron up icon
Monte Carlo Methods Chevron down icon Chevron up icon
Understanding Temporal Difference Learning Chevron down icon Chevron up icon
Case Study – The MAB Problem Chevron down icon Chevron up icon
Deep Learning Foundations Chevron down icon Chevron up icon
A Primer on TensorFlow Chevron down icon Chevron up icon
Deep Q Network and Its Variants Chevron down icon Chevron up icon
Policy Gradient Method Chevron down icon Chevron up icon
Actor-Critic Methods – A2C and A3C Chevron down icon Chevron up icon
Learning DDPG, TD3, and SAC Chevron down icon Chevron up icon
TRPO, PPO, and ACKTR Methods Chevron down icon Chevron up icon
Distributional Reinforcement Learning Chevron down icon Chevron up icon
Imitation Learning and Inverse RL Chevron down icon Chevron up icon
Deep Reinforcement Learning with Stable Baselines Chevron down icon Chevron up icon
Reinforcement Learning Frontiers 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

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(20 Ratings)
5 star 75%
4 star 5%
3 star 5%
2 star 10%
1 star 5%
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shashanth k. Jan 04, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is the best book I have read so far in RL. Please get the second edition and not the first edition. This second edition is completely rewritten and includes so many advanced topics as well. I have read the popular first edition as well. I can say this second edition is completely different from the first edition. So please get this second edition rather than the first edition book.I just wanna thank the author for crafting this masterpiece of a book it is. I have no idea what I would have done without this book. It helped me a big time at work and I can now proudly say that this book made me a pro in RL to deep RL.So to mention again, go for this second edition. My humble thanks to the author again. This book must be a revolution in RL field.
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Mahesh Apr 17, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Wonderful read for beginner like me, complex maths and concepts are clearly explained with examples. Must buy for anyone interested to jump into Reinforcement Learning. Thanks a lot Sudharsan Ravichandiran !!!
Amazon Verified review Amazon
Amazon Customer Jan 22, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I own and have read pretty much all of the DRL books that were published in the past 3 years, and I can with certainty say that this book is by far the best on the subject. An amazing clarity of explanation combined with the vast scope. Thank you so very much Sudharsan!
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Dhruv Nov 16, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Best Deep Reinforcement Learning book available in the market. It covers everything from scratch.Must buy for serious learners.
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Ganesh Nov 06, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I give full marks for ease and elegance with which the topic is dealt with. I had so much struggle learning from the other popular ones. However nothing registered in my mind. This book makes it really easy.Highly recommended.
Amazon Verified review Amazon
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