3 ms·

> the algorithm was able to generalize across multiple problem sets Did it really? I think they reset it and retrained it for each game. I'd like to know how

> the algorithm was able to generalize across multiple problem sets Did it really? I think they reset it and retrained it for each game. I'd like to know how much more is needed to make one instance of the AI that can successfully play any of the games. To play all 49 games that it could learn, does it need to be an extra level deep? Or 49 times larger? Or 2^49 times more?

ZeroFries 12y ago
I think you could establish an upperbound at 50 times larger, because each set of "neurons" could be completely independent for each game, with 49 neurons to determine which set to use. If they have similar features (which they undoubtedly would), the size could drop a lot. Of course, I have no training in neural nets, and so my conclusions are reached from general understanding/reasoning.
DavidSJ 12y ago
What they mean by "the algorithm" is the learning algorithm, not the actual learned behavior. Historically in machine learning a great deal of hand tuning of learning algorithms is required for them to even be effective on a single problem, so it's impressive that a single algorithm was able to learn to play many different games effectively. Having a single instance of an algorithm learn to perform well on many different tasks is really a separate problem than what they're dealing with here.
meric 12y ago
Can one human successfully play all of the games without prior practice in each game? As far as I know a human has to practice almost every game to be able to play all of them without losses. I think for an AI, achieving this standard is a good result - first practicing in each game and then play through all of them without losses.
- bsdetector 12y ago
  A person that learns 1 game will learn the next game much faster, because they have learned a concept such as a bullet or switch or reflection or wrapping. We take this for granted, but there was a time when Breakout was actually marginally fun because it was new. A person that's played all the other games in the list can win Montezuma's Revenge on the first try; this AI can't play Montezuma's Revenge at all.

ZeroFries 12y ago

I think you could establish an upperbound at 50 times larger, because each set of "neurons" could be completely independent for each game, with 49 neurons to determine which set to use. If they have similar features (which they undoubtedly would), the size could drop a lot. Of course, I have no training in neural nets, and so my conclusions are reached from general understanding/reasoning.

DavidSJ 12y ago

What they mean by "the algorithm" is the learning algorithm, not the actual learned behavior. Historically in machine learning a great deal of hand tuning of learning algorithms is required for them to even be effective on a single problem, so it's impressive that a single algorithm was able to learn to play many different games effectively. Having a single instance of an algorithm learn to perform well on many different tasks is really a separate problem than what they're dealing with here.

meric 12y ago

Can one human successfully play all of the games without prior practice in each game? As far as I know a human has to practice almost every game to be able to play all of them without losses. I think for an AI, achieving this standard is a good result - first practicing in each game and then play through all of them without losses.

bsdetector 12y ago
A person that learns 1 game will learn the next game much faster, because they have learned a concept such as a bullet or switch or reflection or wrapping. We take this for granted, but there was a time when Breakout was actually marginally fun because it was new. A person that's played all the other games in the list can win Montezuma's Revenge on the first try; this AI can't play Montezuma's Revenge at all.