Considering there are bots to beat you at Call Of Duty, and Quake, this is not such a feat.
I can't find a reference but I recall an Emulator for NES that used to be able to be player 2 for something like 40 NES games.
I can't find a reference but I recall an Emulator for NES that used to be able to be player 2 for something like 40 NES games.
The research here is not a computer program that plays games. Rather, it's a computer program that (attempts to) play any game, without having any understanding of what it means to play that game, purely by watching the buttons that a human player would press while playing that game.
This bot, on the other hand, understands nothing about the games themselves besides the input keys and the score number(s). It's playing blind.
4 directions and two buttons. You could point a crappy machine learning algo at it, and it would figure it out. That is far fewer variables to manipulate than what a Quake bot has to deal with.
I would bet money I could zap a pigeon every time it died in mario and it would learn to be a master in 12 hours. It won't master CoD anytime soon.
A Quake bot is easier to develop because it is written for a specific game.
I accept that bet for training a pigeon to play SMB :), although the scenario you described isn't comparable (it is only one game and you would be giving the pigeon extra feedback about how to play it. It still seems hard and I think it would be notable if you achieved it.)
There are Guitar Hero Bots taht "OCR" (your words) the screen to play the guitar. Not such a hard trick to read a score or see a turtle. (you know we have self driving cars right?)
The examples you gave are each specific to particular domains.
This whole topic reminds me of how it is much easier to develop natural language processing for use in a particular domain compared to generically.
Also "nintendo" is a particular domain. When you only have a "language" of 8 directions, A, and B, and only time to deal with in response to a screen that side scrolls in only one direction the programming is easy.
A chess engine has more potential decisions for a bishop than this has for a given move. And unlike the the NES, the chess engine has to adjust for changes in the behavior. Given the same input the NES would make the same choices. You can macro through most the games.
1. This was a paper submitted for a joke conference. It is in no way a giant leap in AI. It is something that some nerds find amusing. Like an anti-Rube Goldberg machine. It is trying to accomplish something quite complex using a ridiculously stupid approach.
2. Its stupid because it is looking at the memory state as just a list of numbers that change during the gameplay. It has no knowledge of the actual game. It doesn't parse the memory to figure out game state or anything. It doesn't even have any generic idea of what games involve. It figures out "oo, the number that starts at location 243 goes up in the training data. I'll mash buttons to try to make memory location 243 go up".
It's a cool project. If this is nothing compared to the things you've done, you're welcome to post them- we'd love to see them!
The emulator was presumably programmed to play those 40 games, too.
But the real measure of intelligence is to throw something at the AI that it's never seen before, and see how it performs. And that's the magic of this one: it's a general-purpose video game playing AI. You can find a new game that it wasn't specifically designed to play, and it will quite often do a decent job playing it.