Title: Learning Neural Parsers with Deterministic Differentiable Imitation Learning,
Abstract: We explore the problem of learning to decompose spatial tasks into segments,
as exemplified by the problem of a painting robot covering a large object.
Inspired by the ability of classical decision tree algorithms to construct
structured partitions of their input spaces, we formulate the problem of
decomposing objects into segments as a parsing approach. We make the insight
that the derivation of a parse-tree that decomposes the object into segments
closely resembles a decision tree constructed by ID3, which can be done when
the ground-truth available. We learn to imitate an expert parsing oracle, such
that our neural parser can generalize to parse natural images without ground
truth. We introduce a novel deterministic policy gradient update, DRAG (i.e.,
DeteRministically AGgrevate) in the form of a deterministic actor-critic
variant of AggreVaTeD, to train our neural parser. From another perspective,
our approach is a variant of the Deterministic Policy Gradient suitable for the
imitation learning setting. The deterministic policy representation offered by
training our neural parser with DRAG allows it to outperform state of the art
imitation and reinforcement learning approaches.