I have a question concerning actor critic methods in reinforcement learning.

In these slides (<https://hadovanhasselt.files.wordpress.com/2016/01/pg1.pdf>) different types of actor-critics are explained. Advantage actor critic and TD actor critic are mentioned in the last slide:

[![enter image description here](https://i.stack.imgur.com/jdRXB.jpg)](https://i.stack.imgur.com/jdRXB.jpg)

But when I look at the slide "Estimating the advantage function (2)", it is said, that the advantage function can be approximated by the td error. Then the update rule includes the td error the same way as in TD actor critic.

So is advantage actor critic and td actor critic actually the same? Or is there a difference I don't see?