ec3183a7f107d1b8dbb90cb3c01ea7d5-AuthorFeedback.pdf

Paper ID 10791Title: Information-Theoretic T ask Selection for Meta-Reinforcement LearningWe thank all the reviewers for their thoughtful feedback. Our response can be found below, organized by review.R1 "It is not yet clear how results on such simple "toy" tasks will, if ever, generalize to practically important task distributions. But this current limitation does and should not stop progress towards such seminal contributions."Thank We agree that scalability to more complex settings is challenging (more on this in response to Reviewer 3), but this is a challenge for all of meta-RL. We introduce a method that identifies a clear gap in the literature, and that provides a first solution to the problem, which performs reliably well in a number of current meta-RL benchmarks.