… distribution as opposed to better representations, we show a modification of the previous
experiment where the covariate shift … We note that neither using the covariate shift prediction as …

… While performing favourably on the independent and identically distributed (iid) instances, …
of a distribution shift. To tackle this issue, we propose an ensemble-based deep reinforcement …

… We study the robustness of deep reinforcement learning algorithms against distribution shifts
… While this field is of general interest to the reinforcement learning community, most studies …

… distribution shift in RL, which could cause a decline in expected returns. This impact on
performance is a symptom of overfitting in deep … Deep reinforcement learning at the edge of the …

… 19], where training task distribution may not encompass all real-world scenarios. … resilient
to task distribution shift at test time. We assume the test-time distribution shift to be unknown but …

… Deep neural networks have repeatedly been shown to be non-robust to the uncertainties of
… and naturally occurring distribution shifts wreak havoc on systems relying on deep neural …

… vised deep learning. But how is the performance of winning lottery tickets affected by the
distributional shift inherent to reinforcement … exploration problem, distribution shift and credit …

… Covariate shift essentially refers to the difference in training and testing data distributions. …
Wierstra, and Martin Riedmiller, ‘Playing atari with deep reinforcement learning’, arXiv preprint …

… Deep Reinforcement Learning (RL) has been successfully applied to many different … into
definitions like distributional shift, novelty detection, out-of-distribution or robustness is not only …

… the most effective methods in challenging reinforcement learning problems with large state
… connection to supervised learning under distribution shift. This characterization shows an …