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Deep reinforcement learning has recently been applied to a variety ofrobotics applications, but learning locomotion for robots with unconventionalconfigurations is still limited. Prior work has shown that, despite the simplemodeling of articulated swimmer robots, such systems struggle to find effectivegaits using reinforcement learning due to the heterogeneity of the searchspace. In this work, we leverage insight from geometric models of these robotsin order to focus on promising regions of the space and guide the learningprocess. We demonstrate that our augmented learning technique is able toproduce gaits for different learning goals for swimmer robots in both low andhigh Reynolds number fluids.

Guided Deep Reinforcement Learning for Articulated Swimming Robots