On: Mathematical Foundations of Reinforcement Learning

[dualofdual]

The best lectures on Reinforcement Learning and related topics are by Dimitris Bertsekas: https://web.mit.edu/dimitrib/www/home.html

[rybthrow2 → dualofdual]

Also one by David Silver of Deepmind, AlphaGo fame are good too: https://www.youtube.com/watch?v=2pWv7GOvuf0

[esafak → dualofdual]

His books tend to be dry and geared towards researchers, in my opinion. He has a new one on RL: https://web.mit.edu/dimitrib/www/RLCOURSECOMPLETE%202ndEDITI...

[joe_lin → esafak]

I'm looking for content (researcher myself) -- mainly on the application side. Should I start with this one? Or anything else?

Very curious about RL for LLMs for example (using data from real use).

[esafak → joe_lin]

I have not read it but it looks like a comprehensive reference. For a more applied treatment see Foundations of Deep Reinforcement Learning. https://slm-lab.gitbook.io/slm-lab/publications-and-talks/in...

Neither cover LLMs. I don't follow the literature closely so I can only suggest you read papers: https://github.com/WindyLab/LLM-RL-Papers

[richard___ → dualofdual]

No. They are outdated and focused on strange things. You wont understand ppo from his textbooks

[cplat → richard___]

Which aspects? Foundational textbooks would focus on principles, not necessarily implementations, and don't go "outdated" the same way a snippet does.

[forkerenok → dualofdual]

Would you mind explicitly indicating whether you have reviewed the submitted materials? And if so, why is it inferior to the material you linked?

Not trying to catch you, genuine interest.