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This question is not about the math prerequisites of reinforcement learning, but about the textbooks of mathematics that are enough to understand the literature on reinforcement learning.

What are the mathematics books that are recommended to study in order to understand the majority of the reinforcement learning literature?

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Two books that really helped me to understand RL/deep RL are:

Reinforcement Learning: An Introduction by Andrew Barto and Richard S. Sutton

Deep Reinforcement Learning: Fundamentals, Research and Applications by Hao Dong, Zihan Ding, Shanghang Zhang

Before, I started getting familiar with RL through online courses and papers, but after even coding and research, I read Sutton's book again and found it really great. It deserves to be the reference for RL. Start with it, and you can see how it improves your vision of RL.

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    $\begingroup$ to piggyback onto this answer, I'd also recommend doing some probability/statistics. Enough so you're familiar with (conditional) probability distributions, expectations, etc. $\endgroup$ Commented Jul 12, 2022 at 14:32
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    $\begingroup$ The both are RL books but I am asking for math books. $\endgroup$ Commented Jul 12, 2022 at 14:55
  • $\begingroup$ This doesn't answer the question, as mentioned by the OP above. The question is not: which RL books do you recommend? Of course, some RL books may contain 1-2 sections on math prerequisites, but that wasn't the question. $\endgroup$ Commented Jul 12, 2022 at 15:11
  • $\begingroup$ in my opinion both books are enough for getting involve into RL world. $\endgroup$ Commented Jul 12, 2022 at 20:38

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