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https://arxiv.org/abs/1806.02643 https://arxiv.org/abs/1806.02643 and https://arxiv.org/abs/1803.06376 https://arxiv.org/abs/1803.06376 (as does the choice to d
by gwern 8y ago
https://arxiv.org/abs/1806.02643 https://arxiv.org/abs/1806.02643 and https://arxiv.org/abs/1803.06376 https://arxiv.org/abs/1803.06376 (as does the choice to drop the checkpoints & historical self-plays in general from the training) indicate that AlphaGo versions, at least up until then, tend to be transitive:
> What is worthwhile to observe from the AlphaGo dataset, and illustrated as a series in Figures 3 and 4, is that there is clearly an incremental increase in the strength of the AlphaGo algorithm going from version αr to αrvp, building on previous strengths, without any intransitive behaviour occurring, when only considering a strategy space formed by the AlphaGo versions.
- backpropaganda 8y agoThanks a lot for the links. They look quite interesting. It does seem that Deepmind is aware of this, and are working on evaluating this. Transitivity might be true within AlphaGo versions, but that doesn't give me any confidence that it would also hold when a human is in the equation. If a group of policies more or less occupy the same state space, they are likely to be transitive, but if they occupy disjoint state spaces, I don't think we can be sure of transitivity.