# Tag Info

generally the approach is to have a separate head. For example, imagine you have latent vector $z_k$, you would output two values: $h(z_k)$ and $f(z_k)$ where $0 \leq h \leq 1$ and $b_0 \leq f \leq b_1$ where $b_0$ and $b_1$ are your bounds. In thios setup, during inference you would check $h_k$ and if its greater than some threshold (usually .5), youd ...
You are right, it is sloppy notation by the authors. However, the target network is not necessarily linked to the behaviour policy $\beta$ either. Essentially when they take the expectation with respect to $\rho^\beta$ they are taking expectation with respect to a state distribution induced by some policy $\beta$ that is not necessarily the same as our ...