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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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€11.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (20 Ratings)
Paperback Sep 2020 760 pages 2nd Edition
eBook
€29.99
Paperback
€36.99
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Free Trial
Renews at €11.99p/m
Arrow left icon
Profile Icon Sudharsan Ravichandiran
Arrow right icon
€11.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (20 Ratings)
Paperback Sep 2020 760 pages 2nd Edition
eBook
€29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €11.99p/m
eBook
€29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €11.99p/m

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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 : 9781839210686
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Product Details

Publication date : Sep 30, 2020
Length: 760 pages
Edition : 2nd
Language : English
ISBN-13 : 9781839210686
Category :
Languages :

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Frequently bought together


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Total 134.97
Deep Reinforcement Learning with Python
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Deep Reinforcement Learning Hands-On
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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%
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Rajasekhar Dec 14, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The media could not be loaded. I will give review of this book , every chapter , as of now , I have completed 4 chapters , it's really easy to understand and implement this book
Amazon Verified review Amazon
David Silver May 28, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The most important aspect of most programming books or courses is how well they support learners in writing the code themselves.Ravichandiran’s book cleverly utilizes tools provided by Open AI Gym, along with TensorFlow, to provide lots of short hands-on exercises.
Amazon Verified review Amazon
Rohith Dec 08, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Deep reinforcement learning with python is encyclopedic in coverage of various algorithms.I must appreciate the efforts put by the author to present the most intriguing topics like "Imitation learning" and "Meta reinforcement learning" lucidly and the emphasis that he has made on the progression of topics.This book covers a diverse range of topics ranging from classic RL algorithmslike value iteration, Q learning to the most advanced topics like SAC,A3C, C51, QR-DQN, inverse RL and so..on. special attention has been given to explain frameworks TensorFlow and OpenAI Gym Toolkit.This is another masterpiece from Sudharshan after "Hands-on Deep learning algorithms". This book is highly recommended not only for beginners but also professionals who are involved in RL research
Amazon Verified review Amazon
Anonymous Jan 21, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book with great explanation of concepts
Amazon Verified review Amazon
Client d'Amazon Mar 04, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Molto dettagliato e approfondito, non superficiale. Interesse personale per l'argomento
Amazon Verified review Amazon
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