Approaches

Oracles, teachers and distillation

Let a planner that can see the future label positions, then train a player that cannot see the future to imitate it.

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Oracles, teachers and distillationfeatured

Compressing a legal planner into a network

Labels every legal move with an expensive public-information planner, then trains a small network to imitate it.

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Oracles, teachers and distillation

D4 root-Q clone

Train a tiny fast network to copy how the expensive depth-4 search orders the seven columns.

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Oracles, teachers and distillation

Depth-4 distillation

Tests whether a small model can imitate the reference and finds that a cheap exact search does better.

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Oracles, teachers and distillation

Expert iteration from the depth-4 search

Learn from the strongest search, play with the result, and repeat. After eight rounds, the learned policy was still less than half as good as its teacher.

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Oracles, teachers and distillation

Learning what a long-lived board looks like

Train a classifier to recognise boards from very long games and use it to guide play. It classified almost perfectly but chose worse moves.

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Oracles, teachers and distillation

Phase distillation

Train a fast network to copy a slow but reliable search, using both its chosen column and the values behind that choice.

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