What does this equation mean?

Nothing in this update requires knowing or estimating p(s' $\mid$ s, a) ; it only requires having experienced (s, a, r, s') . A model-based approach instead fits an explicit dynamics model $\hat{p}_\theta(s' \mid s, a)$ — a “world model” — and plans against it directly: $a^{*}_{1:H} = \arg\max_{a_{1:H}} \; \mathbb{E}_{\hat{p}_\theta} \left[ \sum_{t=1}^{H} \gamma^{t} \, r(s_t, a_t) \right]$. A comprehensive survey of the model-based literature frames the trade this way: fitting and planning against $\hat{p}_\theta$ typically buys sample efficiency, because every transition teaches the model something reusable across many hypothetical future plans rather than updating one value estimate, and it buys interpretability, because the model can be queried and its predictions checked against reality independent of the policy it supports; the cost is that policy quality is now bounded by model accuracy, and errors in $\hat{p}_\theta$ compound over the planning horizon H in ways that are hard to detect from the outside [ 6 ] . Classical robotics has practiced a version of this for decades without calling it “model-based reinforcement learning”: model-predictive control on the MIT Cheetah 3 solves a convex optimization over ground reaction forces against a simplified but explicit rigid-body dynamics model, to optimality, in well under half a millisecond per solve at 20 to 30 hertz, producing trot, bound, pace and full three-dimensional gallop gaits on real hardware [ 10 ] . The model there is hand-derived physics rather than a learned network, but the structural bet is the same one Moerland and colleagues describe: an explicit, checkable model of dynamics, planned against at run time.