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Approaches

Each page here is one theory of how to choose a column, grouped by the technique it uses; the engines that play the games and the instruments that measure them live under Engines and Diagnostics.

Techniqueallexpectimaxheuristic-evaluationq-learningn-tuplennuepolicy-gradientevolutionmctsrollout-policy-iterationoracle-distillationrisk-survivalafterstateconstructive-planningdeterminization
Statusallcompletedrejectedruntime-pausedpreregisteredsupport-onlyproposal
Readsallpublicoracle/teacherdiagnostic
Group bytechniquefamily

3 approaches of 87

NNUE evaluators

A small neural network, cheap enough to run at every leaf of a search, that judges a board from its cells.

Read the primer →
65563654656headshand-chosen summariesa vector for every cell and token
NNUE evaluatorsfeatured

Structured NNUE

Give every cell a learned description and predict the game's remaining lifetime. Good predictions still did not produce good choices.

rejectedrecorded
→
456634rise 3U1234567chance statenext disc
NNUE evaluators

Chance-state NNUE

Judge the board at the moment just before the next disc is dealt, so the value never has to be split seven ways.

rejected
→
root123depth 4the net orders; the search decides
NNUE evaluators

Learned-guidance search

Let a small learned board evaluator decide where a deeper search should spend its time, while keeping the exact search as a safety net.

rejectedrecorded
→

No approach page matches that search.

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