  Invariances to translation, rotation and other spatial transformations are a
hallmark of the laws of motion, and have widespread use in the natural sciences
to reduce the dimensionality of systems of equations. In supervised learning,
such as in image classification tasks, rotation, translation and scale
invariances are used to augment training datasets. In this work, we use data
augmentation in a similar way, exploiting symmetry in the quadruped domain of
the DeepMind control suite (Tassa et al. 2018) to add to the trajectories
experienced by the actor in the actor-critic algorithm of Abdolmaleki et al.
(2018). In a data-limited regime, the agent using a set of experiences
augmented through symmetry is able to learn faster. Our approach can be used to
inject knowledge of invariances in the domain and task to augment learning in
robots, and more generally, to speed up learning in realistic robotics
applications.
