  A large part of the interest in model-based reinforcement learning derives
from the potential utility to acquire a forward model capable of strategic long
term decision making. Assuming that an agent succeeds in learning a useful
predictive model, it still requires a mechanism to harness it to generate and
select among competing simulated plans. In this paper, we explore this theme
combining evolutionary algorithmic planning techniques with models learned via
deep learning and variational inference. We demonstrate the approach with an
agent that reliably performs online planning in a set of visual navigation
tasks.
