Review for NeurIPS paper: One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL

Summary and Contributions: The paper proposes an approach to learn diverse behaviors to avoid policies to be too specific for a single task and making them general and robust to variations of the task. The proposed method considers policies depending on latent variables and optimizes an objective that prefers policies with high mutual information between the trajectory and the latent variable, conditioned to the fact that those policies must be \epsilon-optimal. Differently from meta-learning, the training is carried out in a single environment while testing is done on variations. A theoretical study to justify the proposed objective is provided together with an experimental evaluation. Strengths: The main strength point of the paper is the attempt of addressing a quite challenging scenario in which training can be carried out in a single environment while testing should be done in different environments.