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 "Introduction to Probability" 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?