  Deep reinforcement learning has recently been applied to a variety of
robotics applications, but learning locomotion for robots with unconventional
configurations is still limited. Prior work has shown that, despite the simple
modeling of articulated swimmer robots, such systems struggle to find effective
gaits using reinforcement learning due to the heterogeneity of the search
space. In this work, we leverage insight from geometric models of these robots
in order to focus on promising regions of the space and guide the learning
process. We demonstrate that our augmented learning technique is able to
produce gaits for different learning goals for swimmer robots in both low and
high Reynolds number fluids.
