[AstralStorm]

"Observed" after being fed lots of sprites and actual ways on how to play it and actually win at it in the objective function. And it "played" only one kind of game. "Obvious path" riiiight.

In addition, this is wrong to having been said to be new, such attempts have been made before and even stronger in results and generality. For example this (relatively dumb) approach from 2013 generalized kinda well, much better than I've seen a silly even deep network generalize: http://www.cs.cmu.edu/~tom7/mario/

So yes, they are overselling it a lot. I am 100% not impressed by this paper as it lacks critical detail. That it can parse stuff from 2D frames is not interesting, it is basic motion analysis which can be done even by a supremely stupid algorithm, not even a CNN.

I mean, Google best AI can play 15 rooms of a simple game...

[elliottcarlson]

You are comparing a system that learned to play a game (which indeed was very impressive), to a system that learned to make the game by observing the behavior from video. None of your points actually relate to the system described.

[AstralStorm]

By "make" you meant "match some sort of a simple function approximation after hardcoding lots of knowledge about the system and the general function" right? Which is essentially what the neural networks and all the other optimization algorithms were made for?

(The algorithm as described will require a huge database for a game that is even slightly more complicated than Infinite Mario. And we don't even have the sources to try that.)

Even the object motion tracker part will choke in 3D environment. (It is a greedy matcher as they described it.)

Speaking of impressed, Google DeepMind paper is way more actually feasible to improve upon and rich in detail: https://arxiv.org/pdf/1606.01868v1.pdf Compare the two papers in straight quality. I understand why you'd publish any worthless junk in the current academic culture and do not agree we should actually do it.