Well, the paper has an experiment where they train an agent based on their system to play two of the three games (PacMan, PacManMaze and VizDoom) and compare its result to two other systems (an LSTM-based one and a model-based RL one, WorldModel). Their system gets the best score in Pacman but unfortunately the version that does better than WorldModel is the one that doesn't have the memory module whose usefulness is a central claim of their paper. WorldModel outperforms their system in VizDoom by ~300 points, though their system without the memory module "solves" VizDoom (i.e. scores just a bit over than 750; that is 765) and they claim that it's the first Gan-based system that achieves this.

A bit thin of the ground (especially when it comes to supporting the claim about the usefulness of the memory module) but the way neural nets research and especially RL research is going (from what I've seen, not my area of expertise) that's the kind of result that you can expect will get your paper published these days.