What does the Advantage function signify in Dueling Deep Q Networks? I know the Advantage function is related to the Value and State-Action functions. I also understand the following formula -
$$V^{\pi}(s) = \sum_{a \in \mathbb{A}}\pi(a|s)*Q^{\pi}(s,a)$$
But, I am having a hard time understanding this -
$$A(s,a) = Q(s,a) - V(s)$$
I have read the original paper and also various blog posts and am still stuck.

I do not know if there is a different interpretation for Dueling Deep Q Networks specifically, but in general, quoting from the book "Algorithms for Decision Making" (Section 7.3, page 139):

Policies can also be represented using the advantage function, which quantifies the advantage of taking an action in comparison to the greedy action. It is defined in terms of the difference between Q and U:
$$
A(s, a) = Q(s, a) − U(s)
$$
Greedy actions have zero advantage, and nongreedy actions have negative advantage.

$U(s)$ in the above formula is the value function:
$$
U(s) = \max_a Q(s, a).
$$
