If you dive into Reinforcement Learning, you get flooded with a plethora of concepts. Unfortunately, most of the time nobody will explain you the ideas behind the concepts: Why these concepts? What are they good for? What are their limits? How are they related to each other? When to pick what? And so on.
As a result, at least I got quite some things wrong in the beginning which resulted in useless code and more. To (hopefully) give you a better start than I had, I created this talk. It starts at the general idea of Reinforcement Learning and then stepwise moves to the implementation level, explaining the concepts, the whys, hows and limits along the way. Afterwards, some popular additional concepts are briefly shown and why and when they are needed. At the end, some resource pointers to dive deeper are given.
While this is just a first peek into the world of Reinforcement Learning and as always the voice track is missing, I still hope it will make the start into that fascinating topic a bit easier for you.
Update: A recording of the talk (leaving out some details due to time restrictions) can be found at https://www.youtube.com/watch?v=RIEVPxywzu8