Differently from Sarsa and Q-learning, pure temporal difference learning (TD-learning) works with state value functions $V(s)$ and not state-action Q value functions $Q(s,a)$. It means that, in order to select the best action at state $s$, the resulting state $s'$ for every possible action must be computed, so we can get $V(s')$. Thus, we need a model for computing $s'$ from a $(s,a)$ pair. Is that correct? Does that mean that TD-learning can be considered a model-based technique?

Differently from Sarsa and Q-learning, pure temporal difference learning (TD-learning) works with state value functions $V(s)$ and not state-action Q value functions $Q(s,a)$. It means that, in order to select the best action at state $s$, the resulting state $s'$ for every possible action must be computed, so we can get $V(s')$. Thus, we need a model for computing $s'$ from a $(s,a)$ pair. Is that correct? Does that mean that TD-learning can be considered a model-based technique? Or in the case of TD-learning we consider it just a value updating algorithm and ignore the control part, thus removing the need for a model?