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Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more, by Maxim Lapan

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About the Author

Maxim Lapan is a deep learning enthusiast and independent researcher. His background and 15 years' work expertise as a software developer and a systems architect lays from low-level Linux kernel driver development to performance optimization and design of distributed applications working on thousands of servers. With vast work experiences in big data, Machine Learning, and large parallel distributed HPC and nonHPC systems, he has a talent to explain a gist of complicated things in simple words and vivid examples. His current areas of interest lie in practical applications of Deep Learning, such as Deep Natural Language Processing and Deep Reinforcement Learning. Maxim lives in Moscow, Russian Federation, with his family, and he works for an Israeli start-up as a Senior NLP developer.

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Product details

Paperback: 546 pages

Publisher: Packt Publishing (June 21, 2018)

Language: English

ISBN-10: 1788834240

ISBN-13: 978-1788834247

Product Dimensions:

7.5 x 1.2 x 9.2 inches

Shipping Weight: 2.3 pounds (View shipping rates and policies)

Average Customer Review:

4.6 out of 5 stars

14 customer reviews

Amazon Best Sellers Rank:

#31,133 in Books (See Top 100 in Books)

Almost finished Chapter 3, very well written, apparently that the author really know what he is talking about, and able to write clearly. Previous one or two books from Packt disappointed me (lack of depth, cook book style), but this one is a pleasant surprise!

It's a reasonable book on RL. One star off for stating that numpy arrays are tensors, which is false. Tensor has a specific definition describing how it changes under coordinate changes and numpy arrays have no such restriction.Other than that it's ok, not very deep mathematically but as a practical guide definitely a worthy read.

Does a bad job at explaining, while most of the topics were hands-on the book required more googling to figure out what they were talking about.

Im an undergraduate student of Computer Science and I have had an interest in RL for years. This was exactly what I wanted. Theres even a section on stocks with RL. I LOVE THIS BOOK!!!!!!!!!!! YOU ROCK MAX!!!!!!!! THANK YOU!!!!!!!!

Fantastic book. Right combination of theory and practice.

Book intend to add dots between theoretical aspect and modern practical approaches. It will not explain modern approaches with full details, but provide idea with working examples and links for further reading.

I'm impressed by how close to the absolute cutting edge the author takes the reader from zero to RL researcher. I highly recommend to all who are interested in this topic

The book is true to its title, and has a strongly hands-on approach to reinforcement learning. It introduces the reader to the main python libraries used in RL, and some of the most popular environments such as OpenAI Gym and Atari games.The author describes the main RL topics through several examples, while limiting the theory to the minimum necessary, and introduces core concepts only when needed for implementation. For instance, Q-learning does not make its entrance until Chapter 5. The reader unfamiliar with RL will probably find the book easy to follow, while it takes some previous background in machine learning to fully appreciate the characteristics of all the algorithms presented, since the book is (intentionally, I guess) quite brief on the scientific side. On the other hand, the algorithms and implementations are based on very recent publications (often cited through arxiv, so possibly still under review).Overall, this seems like a great starting point for someone who has heard of Deep RL (ideally in a Machine Learning class), and would like to get into the practical aspects of programming it, since libraries such at TensorFlow may otherwise look a bit daunting.

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Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more, by Maxim Lapan PDF

Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more, by Maxim Lapan PDF

Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more, by Maxim Lapan PDF