Review for NeurIPS paper: PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals

This paper proposes using an ensemble of GANs to learn a goal-conditioned forward model of trajectories for use in planning. The model is trained using a variant of hindsight experience replay, resulting in an agent that can succeed at sparse goal-conditioned tasks with much better data efficiency than model-free approaches. All reviewers highlighted the impressiveness of the experimental results, with R1 and R2 finding the approach very interesting, and R3 and R4 indicating the potential impact and interest this work will have. I agree that this paper will likely be of broad interest to the RL community at NeurIPS and therefore recommend acceptance. However, several reviewers also noted the lack of comparison to other model-based approaches.