Review for NeurIPS paper: Multi-Task Reinforcement Learning with Soft Modularization

Summary and Contributions: In this article, the authors present a new method in the field of multi-task Reinforcement Learning. While the method is not restricted to a certain domain, they investigate the method in the experimental part in the application domain of manipulation, using an existing manipulation task benchmark suite (Meta-World). The main issues with multi-task RL that the authors motivate in the introduction and use to motivate their method are: conflicting gradients and balancing optimisation between tasks. They address important issues in multi-task RL that typically hurt the performance gain that we expect in terms of data efficiency and final performance, reported in all major publications in the field. From a high level perspective, there are two main ideas in the paper.