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Currently, I'm only going through these two books

What other introductory books to reinforcement learning do you know, and how do they approach this topic?

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    $\begingroup$ I expect a good answer to provide links to other books but also a brief description of the book and how they treat the subject. $\endgroup$ – nbro Jun 19 at 8:28
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    $\begingroup$ I would like to be able to compose that list in an answer, but currently I'm only going through those two books, the other one was recommended to me, that's why I'm also asking for complementary material, it's always good to have multiple sources. $\endgroup$ – tmaric Jun 19 at 8:32
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In addition to the ones you mentioned, I would add Algorithms of Reinforcement Learning by Csaba Szepesvári. There is a number of professors who use it as a reference in their RL teaching materials (for example this one).

It generally follows the same outline as Sutton & Barto's book (except the part on bandits, it is included in the Chapter on Control). In fact, it may be considered as a condensed version of Sutton & Barto (about 100 pages). In addition, it's freely available online.

I like the author's justification as to why he wrote this book, so I'm just going to quote it:

Why did I write this book? Good question! There exist a good number of really great books on Reinforcement Learning. So why a new book? I had selfish reasons: I wanted a short book, which nevertheless contained the major ideas underlying state-of-the-art RL algorithms (back in 2010), a discussion of their relative strengths and weaknesses, with hints on what is known (and not known, but would be good to know) about these algorithms.

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

This book does not give a detailed background information on Markov Decision Processes, different Bellman equations and relationships between the value function and action-value function, etc. It focuses on Deep Reinforcement Learning and goes straight to Policy and Value - based algorithms using neural networks. It might be good for someone trying to quickly understand what Deep RL algorithms are out there and apply them.

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