Well, there are actually many, many more. Sigma is just the initial seed from which a population of networks rho are created, each evolved by playing against a random previous iteration of themselves. In this way you can say that sigma-rho are just the initial and final point of an entire spectrum of networks, whose only purpose is to create the raw data which are used to train theta, and then be discarded. The stroke of genius of AlphaGo in my opinion was complementing the rollout network, already used in many other programs, with an intuitive network whose purpose is to imitate intuition, and furthermore to create this network by a pool of ‘cheap’ experts (compared to human experts) play. This technology could be adopted in other area where data are prone to be easily and automatically labelled (such as go ending positions).
But haven’t people been having AIs do that—self-play-training—for a long time? I think the most remarkable idea is to use the massive breed of policy nets only to create a judgement net, and use that in the end instead. That’s wild.
Well, there are actually many, many more. Sigma is just the initial seed from which a population of networks rho are created, each evolved by playing against a random previous iteration of themselves. In this way you can say that sigma-rho are just the initial and final point of an entire spectrum of networks, whose only purpose is to create the raw data which are used to train theta, and then be discarded.
The stroke of genius of AlphaGo in my opinion was complementing the rollout network, already used in many other programs, with an intuitive network whose purpose is to imitate intuition, and furthermore to create this network by a pool of ‘cheap’ experts (compared to human experts) play.
This technology could be adopted in other area where data are prone to be easily and automatically labelled (such as go ending positions).
But haven’t people been having AIs do that—self-play-training—for a long time? I think the most remarkable idea is to use the massive breed of policy nets only to create a judgement net, and use that in the end instead. That’s wild.