Reviews: A neurally plausible model learns successor representations in partially observable environments

I don't have any major technical criticisms of the paper. However, I didn't feel that the experimental results really highlighted the advantages of this approach. Specifically, the authors never compare against any other method for solving POMDPs. I think this is necssary for making a compelling case for this method. Is it more sample efficient, more computationally efficient, more flexible?