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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

4 approaches of 87

Expectimax search

Look a few moves ahead, take the best column on your own turns, and average over the discs the game might deal.

Read the primer →
depth 47 · 49 · 343nothing prunedevery branch valued, one column played
Expectimax searchfeatured

Fair expectimax reference (D3/D4)

The reference search looks four moves ahead, averages the sampled chance outcomes, and uses a hand-tuned board evaluator at the bottom.

completedrecorded
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next dischidden valuesample pairs7 × 7 drawn apart7 shared draws
Expectimax searchfeatured

Separate reveal and next-disc sampling

Separates next-disc and hidden-value samples inside search. The change is worth about one extra move of look-ahead.

completedreproduced
→
every outcome5 strataa few outcomes, spent on depth
Expectimax search

Sparse expectimax

Look several moves ahead, but instead of considering every disc the game might deal, take a small fixed handful of representative ones, and always finish the depth you promised.

completed
→
death penaltydepthchance samples3457one constant at its stop, two with room
Expectimax search

Turning the reference search's constants into dials

Makes death penalty, search depth, and chance samples configurable to test which settings improve survival.

completedreproduced
→

No approach page matches that search.

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