# What is a baseline function in policy gradients methods?

Is this same as the Advantage Function?

You actually have a lot of choices for the baseline function. Typical choices are $$\bar{R}$$, the average reward, or $$V(s)$$ the state value function (which can then lead to Actor-Critic methods, if you use it further to calculate a TD error for correcting the policy on each step).
The Advantage function can be treated as a baseline-already-included estimate, because it is $$Q_{\pi}(s,a) - V_{\pi}(s)$$. By using $$A(s,a)$$ as the gradient multiplier for policy gradient update steps, you are implictly using policy gradients with $$V(s)$$ as the baseline.
You might be able to use the Advantage function in some other way to establish a different baseline. However, you should not use the current policy to decide the action in $$A(s,a)$$ if you do so, because that is not independent of the policy gradient. You could use e.g. $$\text{max}_a A(s,a)$$ - I am not sure how well that would work in practice, but I think it would be a valid baseline function.