  Everything else being equal, simpler models should be preferred over more
complex ones. In reinforcement learning (RL), simplicity is typically
quantified on an action-by-action basis -- but this timescale ignores temporal
regularities, like repetitions, often present in sequential strategies. We
therefore propose an RL algorithm that learns to solve tasks with sequences of
actions that are compressible. We explore two possible sources of simple action
sequences: Sequences that can be learned by autoregressive models, and
sequences that are compressible with off-the-shelf data compression algorithms.
Distilling these preferences into sequence priors, we derive a novel
information-theoretic objective that incentivizes agents to learn policies that
maximize rewards while conforming to these priors. We show that the resulting
RL algorithm leads to faster learning, and attains higher returns than
state-of-the-art model-free approaches in a series of continuous control tasks
from the DeepMind Control Suite. These priors also produce a powerful
information-regularized agent that is robust to noisy observations and can
perform open-loop control.
