  This note clarifies some confusions (and perhaps throws out more) around
model-based reinforcement learning and their theoretical understanding in the
context of deep RL. Main topics of discussion are (1) how to reconcile
model-based RL's bad empirical reputation on error compounding with its
superior theoretical properties, and (2) the limitations of empirically popular
losses. For the latter, concrete counterexamples for the "MuZero loss" are
constructed to show that it not only fails in stochastic environments, but also
suffers exponential sample complexity in deterministic environments when data
provides sufficient coverage.
