[Bartweiss]

My complaint is that the path to improving their space is "humans hardcoding endless rule lists".

Section 3.1 of the paper outlines a list of 'hand-authored' functions the agent used to derive events from images. They include animation, sprite-entity relationships, motion, collision, and camera movement. Which is to say, every component of Super Mario level 1-1.

That doesn't mean the paper is uninteresting, or useless. Defining facts based on those possible rules is still an intriguing result. I'm having real trouble working out from the paper how well their agent understood conditional changes like size and fire flowers - if it accurately recreated those rules, then I am impressed.

But "modeled without accessing the code" is a dubious claim about an agent that started with a list of the core rules included in its code. The Engine Learning section (3.2) mentions that automatic derivation of possible facts is a key area for future work. That is to say "this would be flexible if it did feature learning instead of needing feature engineering". Unfortunately, that's the problem in agent design, and the value of CNNs isn't unbeatable performance but the capacity for flexible feature learning. The press release here elides the issue of feature learning entirely when comparing performance.