camlinke

Agreed! A lot of material out there is like the "how to draw and owl" meme: https://imgur.com/gallery/RadSf - start with bandits and now do DDPG.

The goal is for this course to provide the foundations for whatever folks want to do in RL after. It starts with bandits but then covers things like TD, Sarsa, Dyna, etc. in the tabular setting. Then folks learn about more advanced topics like linear and non-linear function approximation (read - linear e.g. Tile Coding, non-linear e.g. neural nets/deep rl).

This very much follows the intro RL course taught by Martha/Adam/Rich at the U of A, and follows Rich's textbook really closely.

A number of us from the lab have been helping to put this course together. It's lead by Martha White and Adam White - two awesome RL profs at the U of A (Martha now leads RLAI) - and is based very heavily on Rich's textbook. The goal is to provide a really strong foundation for those looking to dive deeper into reinforcement learning. It starts with bandits and works all the way up through function approximation, control, policy gradients, and deep RL.

If you have any questions feel free to ask and I'll do my best to answer.

Reinforcement learning has been used in a lot of valuable applications to society/the economy outside of games: control system optimization, robotics, ad targeting, content personalization to name a few. Game playing can often be a great test-bed for RL algorithms that can be applied in other areas.

I chatted with Rich Sutton recently and he said he was writing and update - not sure how far along in the process he is though.

Also Csaba Szepesvári (a colleague of Rich's at the U of A) has a free RL book you can download. http://www.ualberta.ca/~szepesva/RLBook.html