text: In general I find this to be a good paper and vote for acceptance.  The paper is well-written and easy to follow.   The proposed approach is a useful addition to existing literature. Besides that I have not much to say except one point I would like to discuss:  In 4.2 I am not fully convinced of using an adversial model for goal generation.  RL algorithms generally suffer from poor stability   and GANs themselves can have convergence issues.  This imposes another layer of possible instability.  Besides, generating useful reward function, while not trivial, can be seen as easier than solving the full RL problem.  Can the authors argue why this model class was chosen over other, more simple, generative models?   Furthermore, did the authors do experiments with simpler models? Related: "We found that the LSGAN works better than other forms of GAN for our problem."  Was this improvement minor, or major, or didn't even work with other GAN types?  This question is important,  because for me the big question is if this model is universal and stable in a lot of applications or requires careful fine-tuning and monitoring.