What introductory books to reinforcement learning do you know, and how do they approach this topic? Currently, I'm only going through these two books

Reinforcement Learning: An Introduction, by Sutton and Barto: RL explained on an engineering level (mathematical, but readable for a non-mathematician). Elementary notions from probability and statistics are required (conditional probability, total probability theorem, total expectation theorem, and similar. The MIT RES.6-012 &quot;Introduction to Probability&quot; course is a great source of information for these topics.).

Deep Reinforcement Learning, by Miguel Morales: this book introduces the main elements of reinforcement learning in a less formal way than Sutton and Barto (derivations for some equations are not given), using examples to describe the math.


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