Learning to Search via Self-Imitation

We study the problem of learning a good search policy. To do so, we propose the self-imitation learning setting, which builds upon imitation learning in two ways. First, self-imitation uses feedback provided by retrospective analysis of demonstrated search traces. Second, the policy can learn from its own decisions and mistakes without requiring repeated feedback from an external expert. Combined, these two properties allow our approach to iteratively scale up to larger problem sizes than the initial problem size for which expert demonstrations were provided.