14da15db887a4b50efe5c1bc66537089-AuthorFeedback.pdf

We would like to thank the reviewers for their insightful comments. Addressing the common point of limiting our experimentation to a single-decision setting, our intent was to focus our analysis only on the effects of candidate generation. By removing the influences of other factors on the performance of search, for instance, rollout policies and state value function approximations, we can focus the evaluation. We are aware that the sequential-decision setting requires extra reasoning. We would argue, though, that the other components of learning algorithms for search try to ameliorate the amount of reasoning needed --- indeed, learning a perfect value function approximation would essentially reduce a sequential-decision problem to a single-decision problem. However, we do plan on examining our ideas in a full MCTS setting, which we think is a problem deserving its own investigation.