Question on identifiability in the “Dueling Network Architectures for Deep Reinforcement Learning” paper I have two questions on the Dueling DQN paper. First, I have an issue on understanding the identifiability that Dueling DQN paper mentions:

Here is my question: If we have given Q-values $Q(s, a; \theta)$ for all actions, I assume we can get value for state $s$ by:
$$V(s) = \frac {1} {|Q|} \sum_{a \in \mathcal{Q}} Q(s, a; \theta)$$
and the advantage by:
$$A(s,a) = Q(s, a; \theta) - V(s), ~~~ \forall ~a ~in ~\mathcal{A}(s)$$
in which $\mathcal{A}(s)$ is the action space for state $s$. If this is correct, why do we need to have two heads in the network to obtain value and advantage separately?
and then obtain Q-value using
$$Q(s, a; \theta, \alpha, \beta) = V(s; \theta, \beta) + \left( A(s, a; \theta, \alpha) - \max_{a' \in | \mathcal{A} |} A(s, a'; \theta, \alpha) \right). \tag{8}$$
or
$$Q(s, a; \theta, \alpha, \beta) = V (s; \theta, \beta) + \left( A(s, a; \theta, \alpha) − \frac {1} {|A|} \sum_{a' \in \mathcal{A}} A(s, a'; \theta, \alpha) \right). \tag{9}$$
Am I missing something?
My second question is why Dueling DQN does not use the target network as it is used in the DQN paper?
Regarding your first question, $$V^{\pi}(s) = \sum_{a \in A}\pi(a|s)Q^{\pi}(s,a)$$ so recovering the value function from Q really depends on what policy $\pi$ you are using. Hence, you can't really recover the value function $V(s)$ from the $Q(s,a)$ values without knowing your policy distribution for state $s$.
However, you can recover $Q^{\pi}(s,a)$ values if we know $V^{\pi}(s)$ and $A^{\pi}(s,a)$. This is because
$$A^{\pi}(s,a) = V^{\pi}(s,a) - Q^{\pi}(s,a)$$
by definition of advantage. And this is why you need 2 heads to recover the $Q$ values from the Value and Advantage functions. In the original paper, the author's do not use this direct equation to recover $Q^{\pi}(s,a)$ values due to &quot;identifability&quot; issue and the fact that both $V^{\pi}(s)$ and $Q^{\pi}(s,a)$ are only estimates.
Regarding your second question, I believe the author's appllied the Duelling architecture on Double Deep Q Networks, which is an improvement over the single DQN used by Minh et al in learning atari. I do think that you can still use a a target network as in the single DQN case if you wanted to.