  The demand for more transparency of decision-making processes of deep
reinforcement learning agents is greater than ever, due to their increased use
in safety critical and ethically challenging domains such as autonomous
driving. In this empirical study, we address this lack of transparency
following an idea that is inspired by research in the field of neuroscience. We
characterize the learned representations of an agent's policy network through
its activation space and perform partial network ablations to compare the
representations of the healthy and the intentionally damaged networks. We show
that the healthy agent's behavior is characterized by a distinct correlation
pattern between the network's layer activation and the performed actions during
an episode and that network ablations, which cause a strong change of this
pattern, lead to the agent failing its trained control task. Furthermore, the
learned representation of the healthy agent is characterized by a distinct
pattern in its activation space reflecting its different behavioral stages
during an episode, which again, when distorted by network ablations, leads to
the agent failing its trained control task. Concludingly, we argue in favor of
a new perspective on artificial neural networks as objects of empirical
investigations, just as biological neural systems in neuroscientific studies,
paving the way towards a new standard of scientific falsifiability with respect
to research on transparency and interpretability of artificial neural networks.
