I disagree. At the core RL is just updating a table of values, and then using function approximation (aka, machine learning) for more complex cases.
I think it might be perceived as being math heavy because the best resources on the topic [0][1] use a lot of math notation to express their ideas. The ideas are subtle and easy to mess up though. I think these books are some of the first that made me appreciate math notation, it can look scary, but it does convey the ideas more accurately than words can.
I think it might be perceived as being math heavy because the best resources on the topic [0][1] use a lot of math notation to express their ideas. The ideas are subtle and easy to mess up though. I think these books are some of the first that made me appreciate math notation, it can look scary, but it does convey the ideas more accurately than words can.
[0] http://incompleteideas.net/ (his "Reinforcement Learning: An Introduction" is a great book)
[1] https://www.manning.com/books/grokking-deep-reinforcement-le... looks promising, I've read the few chapters available and was impressed. It's still an early work with grammatical mistakes, but the layout of the ideas was clear and organized.