The only things that are required, I believe, is that the full state of the game can be fed into the network as input, and that the action space is small enough to be represented by network output and is discrete, which allows MCTS to be used. If you can transform an arbitrary problem into this formulation then in theory the same methods can be used.
I agree with this, although it might not work for some theoretically possible games that humans would not actually play.
Life in the real world, however, is not a perfect information zero-sum game, or even an approximation of one. So there is no reason to suppose that the techniques use will generalize to a fooming AI.
The general tool: residual networks variant of convolutional NNs, MCTS-like variable-depth tree search. Prerequisites: input can be presented as K layers of N-D data (where N=1,2,3… not too large), the action space is discrete. If the actions are not discrete, an additional small module would be needed to quantize the action space based on the neural network’s action priors.
For those of us without a lot of background on AlphaGo Zero, does anyone care to summarize how general the tool used to create it is likely to be?
The only things that are required, I believe, is that the full state of the game can be fed into the network as input, and that the action space is small enough to be represented by network output and is discrete, which allows MCTS to be used. If you can transform an arbitrary problem into this formulation then in theory the same methods can be used.
As far as I can see you can use the same techniques to learn to play any perfect information zero-sum game
Is there any reason why the same techniques couldn’t be applied to imperfect information non-zero-sum games?
I agree with this, although it might not work for some theoretically possible games that humans would not actually play.
Life in the real world, however, is not a perfect information zero-sum game, or even an approximation of one. So there is no reason to suppose that the techniques use will generalize to a fooming AI.
Here are some examples of recent work that uses these same tools to make other critical components of a more general AI:
https://coxlab.github.io/prednet/
https://arxiv.org/abs/1707.06203
https://deepmind.com/blog/differentiable-neural-computers/
(Edit note: Made the links into actual links, let me know if you do not want me to fix/improve small things like this in future comments of yours)
The general tool: residual networks variant of convolutional NNs, MCTS-like variable-depth tree search. Prerequisites: input can be presented as K layers of N-D data (where N=1,2,3… not too large), the action space is discrete. If the actions are not discrete, an additional small module would be needed to quantize the action space based on the neural network’s action priors.