Reviews: Meta-Reinforcement Learning of Structured Exploration Strategies

I have increased my score rom a 4 to a 7. The main concern in my original review was that the reward signal was switched from dense to sparse rewards for evaluation but I am now convinced that it's a reasonable domain for analysis. I think an explicit discussion on switching the reward signal would be useful to include in the final version of the paper. They show how to use MAML to update the distribution of the latent state in addition to using standard MAML to update the parameters of the policy. They empirically show that these policies learn a reasonable exploration policy in sparse-reward manipulation and locomation domains. I like the semantics of using MAML to update the distribution of a policy's stochastic latent state.