Review for NeurIPS paper: Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control

The work proposed a simple multi-task RL approach to continuous control through two-stage training, an offline stage with policy distillation and an online stage to fine-tune the meta-policy with online transitions collected from interacting with actual environment. The paper overall is well written and easy to understand. Reviewers appreciate the extensiveness of the experiments and ablation studies demonstrating the effectiveness of the proposed approach. It is encouraging to see the simple framework achieve significant boost over state-of-the-art multi-task RL approach.