They performed well enough to make us believe that we can seriously spend $$$ to train a model to generate images that look real.

StyleGAN was the first time "deep fakes" became a real thing.

I could never remember the git commands beyond commit/push/pull, but I find that chatGPT is very good at helping me with it whenever I need

Thanks. At a high level, I see search heuristics to be quite similar to value functions. In the book, we prove A* optimality by "reward shaping" Dijkstra's algorithm, which is another connection between search heuristics and values.

There's a fairly large RL Theory community.

Completely agree.

Recently I got a review (for a journal) that was from a relevant expert who also signed his name at the end (he didn't need to). Made us take the review very very seriously, even though it was negative.

ahm ahm... we just posted this today :)
bsky.app/profile/aviv...

Yes, when I teach I also have a final "hand waving" class on deep RL where I show how to go from the textbook material to DQN, PPO, Alpha Go. Adding such comments is a good idea, thanks!

We don't really cover deep RL algorithms. There's a lot on Q learning, and the distance from what we cover to DQN is very small. Actually, it would be a good idea to add a remark on that, thanks!

🎯

We hope you find it useful!

The book is still work in progress - we’d be grateful for comments, suggestions, omissions, and errors of any kind, at [email protected]

But for teaching RL, we wanted a book that is both rigorous (full proofs and analytical examples), covers what we feel is most relevant, and is easy enough for undergrad teaching.

The book is a focused one semester course for advanced undergrad/early grad that covers key topics in depth.

For teachers, we also have a 40+ page exam booklet on our website.

Why this book? 


There are several other excellent textbooks, including Sutton and Barto and Bertsekas and Tsitsiklis.

The "bad reviewer party" sounds much more fun tho

yes, this is a great paper!