the network is represented by a vector $S(t) = (S_l(t), \forall l \in N)$. The relocation of capacity modules can be represented as vectors $R(t)=(R_a(t),\forall a\in A)$. Accordingly, there are ${I_0+|L|-1 \choose I_0}$ possible arrangements of $I_0$ modules over $|L|$ locations. In the case where $I_0 \geq |L|$ and that each location gets at least one module, there are ${I_0-1 \choose |L|-1}$ possible arrangements. In this realistic context, access hub networks are expected to be composed of a high number of locations (i.e., hundreds). Thus, state and action spaces would be significantly large-sized, which results in curse of dimensionality issues ([@powell2007approximate]).\
Moreover, a set of realization scenarios $\omega \in \Omega$ with probability $\phi_{\omega}$ is considered. The number of pickups and deliveries as well as the storage volume requirements are observed hourly and respectively represented as a vectors $\rho^P(\tau, \omega) = (\rho^P_l(\tau,\omega), \forall l \in L)$, $\rho^D(\tau, \omega) = (\rho^D_l(\tau, \omega), \forall l \in L)$ and $D(\tau, \omega)=(D_l(\tau, \omega), \forall l \in L)$, for every operations hour $\tau \in T_t$, where $t \in T$ is an operations horizon between two deployment periods (e.g. a week). If a courier or rider observes a lack of storage capacity when visiting an access hub, 