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The author is wildly extrapolating limited results ("Nash Equilibrium outcomes achieved by agents trained using deep reinforcement learning") to the entire fiel

The author is wildly extrapolating limited results ("Nash Equilibrium outcomes achieved by agents trained using deep reinforcement learning") to the entire field. Just because in DRL agents approach nash equilibrium in limited set of games does not means that somehow using nash equilibrium is going to help design future algorithms. E.g. for several games such as Pong, Go on which we know that DRL outperforms traditional methods, there is no tractable Nash equilibrium. Also let alone so many other problem which come under umbrella of deep learning such as recognition / detection/ Seq to Sequence prediction where there is no "Game theory" component.

pizza 10y ago
The author's description of "approximation of Nash equilibrium" is a description of the use of contradiction in the time-evolution of the two internal components of GANs
westoncb 10y ago
My understanding is that the use of game theory in this context has nothing to do with the domain in which the network is being trained. It's not about creating game playing AI's—it's about a 'game' between two neural networks, one who competes by getting better at classifying, and the other by getting better at generating. That basic architecture is quite general—which I think is why you also see e.g. LeCun being excited about this direction. To be fair, the article does a kind of weird thing in discussing three different ways game theory has shown up in the context of machine learning recently—but they're all separate ideas really. Edit: a more precise description in the abstract here: https://arxiv.org/abs/1406.2661 https://arxiv.org/abs/1406.2661
webmaven 10y ago
> Also let alone so many other problem which come under umbrella of deep learning such as recognition / detection/ Seq to Sequence prediction where there is no "Game theory" component. Ah, but there is! GANS can be applied to all those problems.

pizza 10y ago

The author's description of "approximation of Nash equilibrium" is a description of the use of contradiction in the time-evolution of the two internal components of GANs

westoncb 10y ago

My understanding is that the use of game theory in this context has nothing to do with the domain in which the network is being trained. It's not about creating game playing AI's—it's about a 'game' between two neural networks, one who competes by getting better at classifying, and the other by getting better at generating. That basic architecture is quite general—which I think is why you also see e.g. LeCun being excited about this direction. To be fair, the article does a kind of weird thing in discussing three different ways game theory has shown up in the context of machine learning recently—but they're all separate ideas really. Edit: a more precise description in the abstract here: https://arxiv.org/abs/1406.2661 https://arxiv.org/abs/1406.2661

webmaven 10y ago

> Also let alone so many other problem which come under umbrella of deep learning such as recognition / detection/ Seq to Sequence prediction where there is no "Game theory" component. Ah, but there is! GANS can be applied to all those problems.