  Model-based Deep Reinforcement Learning (RL) assumes the availability of a
model of an environment's underlying transition dynamics. This model can be
used to predict future effects of an agent's possible actions. When no such
model is available, it is possible to learn an approximation of the real
environment, e.g. by using generative neural networks, sometimes also called
World Models. As most real-world environments are stochastic in nature and the
transition dynamics are oftentimes multimodal, it is important to use a
modelling technique that is able to reflect this multimodal uncertainty. In
order to safely deploy such learning systems in the real world, especially in
an industrial context, it is paramount to consider these uncertainties. In this
work, we analyze existing and propose new metrics for the detection and
quantification of multimodal uncertainty in RL based World Models. The correct
modelling & detection of uncertain future states lays the foundation for
handling critical situations in a safe way, which is a prerequisite for
deploying RL systems in real-world settings.
