  Having access to a forward model enables the use of planning algorithms such
as Monte Carlo Tree Search and Rolling Horizon Evolution. Where a model is
unavailable, a natural aim is to learn a model that reflects accurately the
dynamics of the environment. In many situations it might not be possible and
minimal glitches in the model may lead to poor performance and failure. This
paper explores the problem of model misspecification through uncertainty-aware
reinforcement learning agents. We propose a bootstrapped multi-headed neural
network that learns the distribution of future states and rewards. We
experiment with a number of schemes to extract the most likely predictions.
Moreover, we also introduce a global error correction filter that applies
high-level constraints guided by the context provided through the predictive
distribution. We illustrate our approach on Minipacman. The evaluation
demonstrates that when dealing with imperfect models, our methods exhibit
increased performance and stability, both in terms of model accuracy and in its
use within a planning algorithm.
