When Waiting is not an Option : Learning Options with a Deliberation
  Cost
  Recent work has shown that temporally extended actions (options) can be
learned fully end-to-end as opposed to being specified in advance. While the
problem of "how" to learn options is increasingly well understood, the question
of "what" good options should be has remained elusive. We formulate our answer
to what "good" options should be in the bounded rationality framework (Simon,
1957) through the notion of deliberation cost. We then derive practical
gradient-based learning algorithms to implement this objective. Our results in
the Arcade Learning Environment (ALE) show increased performance and
interpretability.
