Review for NeurIPS paper: Information-theoretic Task Selection for Meta-Reinforcement Learning

This paper was quite controversial among the four reviewers, leading to more than 10 pages of discussion (longer than the paper itself!) In the end, two reviewers were advocating for acceptance (R1, R3), one was advocating for rejection (R2), and one was leaning towards rejection (R4). This is a direction that hasn't been studied before, and will likely become quite relevant in settings where the task distribution is quite heterogeneous The experimental results suggest that the algorithm performs very well on a large number of simple domains, when combined with MAML and RL 2. The experiments also include an ablation study. Time complexity is not an issue; the reviewers appreciated the author response here. These are the main reasons that R1 and R3 were advocating for acceptance. I agree that these are strong points, and make me want to accept the paper.