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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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

$48.99

$79.99

Mastering Reinforcement Learning with Python

$48.99

Total $ 177.97

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Amazon verified reviews

shashanth k. Jan 04, 2021

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.

Amazon Verified review

Mahesh Apr 17, 2021

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 Customer Jan 22, 2021

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!

Dhruv Nov 16, 2020

Best Deep Reinforcement Learning book available in the market. It covers everything from scratch.Must buy for serious learners.

Ganesh Nov 06, 2023

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.

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

What do you get with eBook?