Any references you have would be greatly appreciated.

IMO the best intro book is Sutton's [1], it's extremely accessible (little math background needed) and covers all basic concepts. Work through David Silver's course (search youtube, it overlaps heavily with the book above) and then you are ready for something more advanced like [2] and you can start reading and implementing research papers.

[1] http://incompleteideas.net/book/the-book.html

[2] http://rail.eecs.berkeley.edu/deeprlcourse/

this is exactly what i was hoping for- thank you!

I think the idea is not to replace learning the theory or math, but rather to just postpone it. Learning the practical aspects of an engineering discipline can provide the necessary motivation to study the theory/math, and this is the oft ignored factor. I also think there is some benefit in learning a topic using the tool that you will actually use in production (if that is possible without adding unnecessary complexity in the syllabus).

I personally learned DRL from David Silver's course and Sutton & Burto back in the days. They were the only good resources around and I liked them very much. But I think that with the advent of high-level frameworks in DRL, there are better learning paths.

I do intend to teach the theory/math in a later installment of this series, but I wanted to do it by showing students how to implement the various classes of algorithms e.g. Q-learning (DQN/Rainbow), policy gradients (PPO) and model-based (AlphaZero) using RLlib. This would kill two birds with one stone: you can simultaneously pick up the theory/math and the lower level API of the tool that you will be using in the future anyway.