Reviews: MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies

The authors propose a method of combining multiple sub-policies with continuous action spaces by multiplicative composition (instead of the standard additive model in options,etc.). The sub policies are pre-trained with imitation learning. MCP shows competitive or much better results than other state of the art hierarchical and latent space methods on challenging high-dimensional domains (the T-Rex playing soccer!). Pros: 1) The idea is clearly written and with several details for re-implementation 2) Compelling results on challenging environments 3) Good baseline comparisons with very recent papers 4) The analysis with latent space methods is really appreciated Cons: 1) I'm not sure how novel the idea is, as there is a lot of literature on using an ensemble or mixture of experts/dynamics models/policies, etc. That being said, the results are very compelling.