Hey HN, I wrote an intro to reinforcement learning (RL) tutorial that tries to balance technical depth and high-level breadth. I hope this can be a useful intro to the field for people who are interested in RL, but don't want to read tons of papers that assume lots of experience with the basic ideas. I've also tried to position this to outline the methods and style of the field, so you can use your excitement/interest from this post to help gauge whether or not you want to explore RL more deeply. At the end of the post I've curated and described some of the best online (and free) resources you can explore if you want to learn more.

I hope people find this useful, and I'd also appreciate feedback and constructive criticism on the writing style, content, organization, etc.

Thanks!