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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

Monte Carlo tree search

Grow the look-ahead only where it seems promising, guided by quick simulated playouts.

Read the primer →
4163sampled futuresone node per visible state
Monte Carlo tree searchfeatured

Observable-state MCTS

Grow the look-ahead by simulation, but only ever let the search know what a player would actually be able to see.

rejectedrecorded
→
priorvisitsvisits outgrow the prior
Monte Carlo tree search

PUCT

A complete but unrun tree search that starts from a prior about which columns are worth trying.

→
selectexpandsimulateback up
Monte Carlo tree search

TypeScript MCTS lab

A small Monte Carlo tree search you can run in a terminal and watch one decision at a time.

rejected
→

No approach page matches that search.

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