Reviews: SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies

The paper introduces a scalable approach for doing meta inverse RL based on maximum entropy IRL. The baseline is a meta-learning method based on behavioral cloning over which a significant performance improvement is obtained, Pro: The approach seems technically sound, building on the theory of AIRL/GAIL. Also, implementing the equations in a practical and efficient way is a non-trivial contribution. Furthermore, the paper is clearly written. The motivation for IRL versus BC and the advantages that IL can have over RL are clearly explained.