Counterfactual Regret Minimization (AGT 26)

Davidson CSC 383: Algorithmic Game Theory, S23.
Week 14 - Wednesday.

[@kimchangjun5311]

Amazing step by step example for CFR !!!

[@aptor]

This is by far the best explained CFR lesson. Thank you for doing this!

[@5th_Interaction]

Beautifully articulated.

[@michaelschoenhals376]

thanks a ton for this! great video.

[@ruairi-spain]

More videos please! Great work, you make hard things easier to visualise. I hope you get back to making KZbin videos ❤

[@sounakmojumder5689]

Hi, thank you do you have any pytorch application?

[@bertobertoberto242]

DS student here, trying to grasp DeepCFR, amazing video... just wanted to point out a thing... the "P(K|Qb)" you say that you use Bayes rule and you phrase it like "P(P1 would play bet when they have a king)/P(P1 would play bet overall)"... I might be mistaking, but i don't see how you are applying Bayes... instead, I got your same result using the definition of conditional probability P(a|b) = P(a,b)/P(b)... because I get "P(KQb)/P(Qb)", now Q and b/K are independent events, so "[P(Kb)P(Q)]/[P(Q)P(b)]"... from here, applying the definition of joint probability and total probability, I get your same final probability Apart from this, thank you so much

[@jonnyx850]

You're correct, he's not using baye's rule/thm. It's the definition of conditional probability in Bayesian stats that he's using. It's actually fairly common for people to mix them up. Nbd

[@CsabaSzepesvari]

Some people actually say that Bayes' rule/thm = definition of conditional probability. In a way, they are not completely wrong:)

[@julienescaig6317]

You are fucking awesome Bro 🙏