  We study the problem of learning a good search policy for combinatorial
search spaces. We propose retrospective imitation learning, which, after
initial training by an expert, improves itself by learning from
\textit{retrospective inspections} of its own roll-outs. That is, when the
policy eventually reaches a feasible solution in a combinatorial search tree
after making mistakes and backtracks, it retrospectively constructs an improved
search trace to the solution by removing backtracks, which is then used to
further train the policy. A key feature of our approach is that it can
iteratively scale up, or transfer, to larger problem sizes than those solved by
the initial expert demonstrations, thus dramatically expanding its
applicability beyond that of conventional imitation learning. We showcase the
effectiveness of our approach on a range of tasks, including synthetic maze
solving and combinatorial problems expressed as integer programs.
