Reviews: Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs

Originality: The authors make clear the distinction from related work. They are not the first to integrate GANs for generating goals in RL, but do so in a new and interesting way. Quality: The comparison with baselines is thorough, showing the benefit of this approach for these domains. However, page 7 claims that HALGAN RL agents need fewer samples than standard RL, and yet in fact HALGAN must be exposed to enough samples of successful trajectories to be able to effectively hallucinate goal states. Are the 1000-samples used to train the HALGAN shown in Figure 3(f) 1000 examples of /goal/ states, or just states in general.