Understanding the equation of TD(0) in the paper "Learning to predict by the methods of temporal differences"
Why isn't it wise for us to completely erase our old Q value and replace it with the calculated Q value?
Why is $M_t$ (the emphasis) helping in correcting for the state distribution in the Emphatic TD algorithm?
By learning from incomplete episodes, does David Silver mean learning of $V(s)$ even when the episode is not completed?
Only top scored, non community-wiki answers of a minimum length are eligible