text: The submission tackles an important problem of learning and transferring multiple motor skills.  The approach relies on using an embedding space defined by latent variables and entropy-regularized policy gradient / variational inference formulation that encourages diversity and identifiability in latent space. The exposition is clear and the method is well-motivated.  I see no issues with the mathematical correctness of the claims made in the paper.  The experimental results are both instructive of how the algorithm operates (in the particle example), and contain impressive robotic results.  I appreciated the experiments that investigated cases where true number of tasks and the parameter T differ, showing that the approach is robust to choice of T. The submission focuses particularly on discrete tasks and learning to sequence discrete tasks (as training requires a one-hot task ID input).  I would like a bit of discussion on whether parameterized skills (that have continuous space of target location, or environment parameters, for example) can be supported in the current formulation, and what would be necessary if not. Overall, I believe this is in interesting piece of work at a fruitful intersection of reinforcement learning and variational inference,  and I believe would be of interest to ICLR community.