  We propose regularity as a novel reward signal for intrinsically-motivated
reinforcement learning. Taking inspiration from child development, we postulate
that striving for structure and order helps guide exploration towards a
subspace of tasks that are not favored by naive uncertainty-based intrinsic
rewards. Our generalized formulation of Regularity as Intrinsic Reward (RaIR)
allows us to operationalize it within model-based reinforcement learning. In a
synthetic environment, we showcase the plethora of structured patterns that can
emerge from pursuing this regularity objective. We also demonstrate the
strength of our method in a multi-object robotic manipulation environment. We
incorporate RaIR into free play and use it to complement the model's epistemic
uncertainty as an intrinsic reward. Doing so, we witness the autonomous
construction of towers and other regular structures during free play, which
leads to a substantial improvement in zero-shot downstream task performance on
assembly tasks.
