  In recent years, a growing number of deep model-based reinforcement learning
(RL) methods have been introduced. The interest in deep model-based RL is not
surprising, given its many potential benefits, such as higher sample efficiency
and the potential for fast adaption to changes in the environment. However, we
demonstrate, using an improved version of the recently introduced Local Change
Adaptation (LoCA) setup, that well-known model-based methods such as PlaNet and
DreamerV2 perform poorly in their ability to adapt to local environmental
changes. Combined with prior work that made a similar observation about the
other popular model-based method, MuZero, a trend appears to emerge, suggesting
that current deep model-based methods have serious limitations. We dive deeper
into the causes of this poor performance, by identifying elements that hurt
adaptive behavior and linking these to underlying techniques frequently used in
deep model-based RL. We empirically validate these insights in the case of
linear function approximation by demonstrating that a modified version of
linear Dyna achieves effective adaptation to local changes. Furthermore, we
provide detailed insights into the challenges of building an adaptive nonlinear
model-based method, by experimenting with a nonlinear version of Dyna.
