  A desirable property of an intelligent agent is its ability to understand its
environment to quickly generalize to novel tasks and compose simpler tasks into
more complex ones. If the environment has geometric or arithmetic structure,
the agent should exploit these for faster generalization. Building on recent
work that augments the environment with user-specified attributes, we show that
further equipping these attributes with the appropriate geometric and
arithmetic structure brings substantial gains in sample complexity.
