  Poor sample efficiency is a major limitation of deep reinforcement learning
in many domains. This work presents an attention-based method to project neural
network inputs into an efficient representation space that is invariant under
changes to input ordering. We show that our proposed representation results in
an input space that is a factor of $m!$ smaller for inputs of $m$ objects. We
also show that our method is able to represent inputs over variable numbers of
objects. Our experiments demonstrate improvements in sample efficiency for
policy gradient methods on a variety of tasks. We show that our representation
allows us to solve problems that are otherwise intractable when using na\"ive
approaches.
