  Nowadays, Deep Learning (DL) methods often overcome the limitations of
traditional signal processing approaches. Nevertheless, DL methods are barely
applied in real-life applications. This is mainly due to limited robustness and
distributional shift between training and test data. To this end, recent work
has proposed uncertainty mechanisms to increase their reliability. Besides,
meta-learning aims at improving the generalization capability of DL models. By
taking advantage of that, this paper proposes an uncertainty-based
Meta-Reinforcement Learning (Meta-RL) approach with Out-of-Distribution (OOD)
detection. The presented method performs a given task in unseen environments
and provides information about its complexity. This is done by determining
first and second-order statistics on the estimated reward. Using information
about its complexity, the proposed algorithm is able to point out when tracking
is reliable. To evaluate the proposed method, we benchmark it on a
radar-tracking dataset. There, we show that our method outperforms related
Meta-RL approaches on unseen tracking scenarios in peak performance by 16% and
the baseline by 35% while detecting OOD data with an F1-Score of 72%. This
shows that our method is robust to environmental changes and reliably detects
OOD scenarios.
