  Deep neural policies have recently been installed in a diverse range of
settings, from biotechnology to automated financial systems. However, the
utilization of deep neural networks to approximate the value function leads to
concerns on the decision boundary stability, in particular, with regard to the
sensitivity of policy decision making to indiscernible, non-robust features due
to highly non-convex and complex deep neural manifolds. These concerns
constitute an obstruction to understanding the reasoning made by deep neural
policies, and their foundational limitations. Hence, it is crucial to develop
techniques that aim to understand the sensitivities in the learnt
representations of neural network policies. To achieve this we introduce a
theoretically founded method that provides a systematic analysis of the
unstable directions in the deep neural policy decision boundary across both
time and space. Through experiments in the Arcade Learning Environment (ALE),
we demonstrate the effectiveness of our technique for identifying correlated
directions of instability, and for measuring how sample shifts remold the set
of sensitive directions in the neural policy landscape. Most importantly, we
demonstrate that state-of-the-art robust training techniques yield learning of
disjoint unstable directions, with dramatically larger oscillations over time,
when compared to standard training. We believe our results reveal the
fundamental properties of the decision process made by reinforcement learning
policies, and can help in constructing reliable and robust deep neural
policies.
