So... the original DQN paper already did Space Invaders as part of the ALE environment, getting better than their human player's score. The DeepMind source code as well as other reimplementations have been out for years, and likewise for Double Q-learning, so it didn't really need someone to reimplement it either. Looking at this paper, I'm not sure what is new or interesting, other than the experiment with using the Atari RAM as features (but even that doesn't seem as interesting as the paper which used MCTS on the RAM: "Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning", Guo et al 2014 http://papers.nips.cc/paper/5421-deep-learning-for-real-time... ). Or is this just the report from an (impressive) student project and not intended to be groundbreaking research?
Just a nitpick, but the original didn't do better than human scores. It does better than their human panel, who seem to suck at space invaders and a whole bunch of other games. In general DQN does worse than expected at space invaders for some reason (compared to, say, galaxian, a very similar game, at which it does very well)
