Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series) 1st Edition, Kindle Edition

Overall, the book is decent. The writing is clear and the amount of information on each technique is good - just detailed enough that you can understand it, without excessive detail. It also covers a nice selection of topics. The selection of methods should leave the reader in a good place to learn the more advanced stuff on their own.However, the authors make the same mistake countless other instructors make - with the exception of a single, self-contained example for REINFORCE, all the examples in the book make use of their own personal framework (the SLM lab). This is done in an effort to reduce boilerplate, but it means the examples are no longer self-contained and it adds the additional burden of learning their framework, which I'm entirely uninterested in doing because in practice I will use my own. Ideally, all the examples would be pure PyTorch, and each should entirely self-contained in a single file so you can run the example as is, without learning a new framework or having to reference other modules. Obviously nobody would write production code this way, but for pedagogic purposes, this is by far the easiest way to learn. Note to instructors everywhere: we're not interested in your framework, no matter how proud of it you are. It doesn't belong in an instructional text.Otherwise, it's a very good book and despite my complaint, I would recommend it.

Arrived in just one day (Thank you Amazon). The book is very well written. The key concepts are clearly explained and easy to understand. I definitely recommend this book to anyone that wants to grab the key ideas and start working on Deep RL right away.I also recommend the book from Sutton and Barto as a supplement to this book if you want to dive deeper into theoretical RL.

Este es un buen libro de consulta.

Wow, what a book! This is exactly what I was looking for: a hands-on introduction to Deep RL that covers all of the prevailing contemporary theory and applications. To boot, the authors' associated SLM Lab GitHub repo (covered extensively throughout the book) makes it easy to construct, deploy, and optimize state-of-the-art agents across a broad range of environments. Thank you, Laura and Keng :)

The authors have taken great pains to keep the book relevant to the times. The fact that a companion git repo is available to try out what you’re reading about in each chapter shows the Theory and Practice approach they describe. This is exactly the right way to learn a complex new scienceThe theory in the chapters are easy to digest, quick links, code and snippets available to try out on SLM lab, and a great reference to get back to when putting Deep RL to work in your real world applications. Highly recommend to anyone who’s beginning to explore RL and wants to start using it to build robot arms, game agents, or achieve AGI

This book was very clear and well-written. A very useful reference regardless of level of background in the subject. Insightful for readers of all levels.

Not a page wasted for irrelevant detours nor unnecessary lengthy paragraphs for conveying some concepts that should be easily understood by whoever is capable of diving into deep RL. Only issue, pyTorch must have been used for better community adoptation.

Exactly the book I was looking for. Really well written and the important ideas are very well condensed. Read this before sutton and barto.

Das Buch behandelt alle Grundlagen zu DRL. Die Erklärungen sind klar und kurz beschrieben. Gibt keine zeitverschwendung, geht direkt zu den Fundamenten, und zitierte Artikel zur weiteren Lektüren (das Nützlichste).Die Papierqualität ist gut. Der Source Code ist nicht hilfreich. sobald es verstanden ist, ist es sehr einfach zu implementieren.

Super geschrieben, klar und verständlich. Code Beispiele auch sehr hilfreich.