  Although deep reinforcement learning has advanced significantly over the past
several years, sample efficiency remains a major challenge. Careful choice of
input representations can help improve efficiency depending on the structure
present in the problem. In this work, we present an attention-based method to
project inputs into an efficient representation space that is invariant under
changes to input ordering. We show that our proposed representation results in
a search space that is a factor of m! smaller for inputs of m 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 naive approaches.
