  Deep model-based reinforcement learning methods offer a conceptually simple
approach to the decision-making and control problem: use learning for the
purpose of estimating an approximate dynamics model, and offload the rest of
the work to classical trajectory optimization. However, this combination has a
number of empirical shortcomings, limiting the usefulness of model-based
methods in practice. The dual purpose of this thesis is to study the reasons
for these shortcomings and to propose solutions for the uncovered problems.
Along the way, we highlight how inference techniques from the contemporary
generative modeling toolbox, including beam search, classifier-guided sampling,
and image inpainting, can be reinterpreted as viable planning strategies for
reinforcement learning problems.
