  Recurrent neural network-based reinforcement learning systems are capable of
complex motor control tasks such as locomotion and manipulation, however, much
of their underlying mechanisms still remain difficult to interpret. Our aim is
to leverage computational neuroscience methodologies to understanding the
population-level activity of robust robot locomotion controllers. Our
investigation begins by analyzing topological structure, discovering that
fragile controllers have a higher number of fixed points with unstable
directions, resulting in poorer balance when instructed to stand in place.
Next, we analyze the forced response of the system by applying targeted neural
perturbations along directions of dominant population-level activity. We find
evidence that recurrent state dynamics are structured and low-dimensional
during walking, which aligns with primate studies. Additionally, when recurrent
states are perturbed to zero, fragile agents continue to walk, which is
indicative of a stronger reliance on sensory input and weaker recurrence.
