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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

Fair expectimax

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

columnsdepth 4depth 5a fifth move on two columns, not seven
Fair expectimax

Looking five moves ahead

Three attempts add a fifth move for selected columns, at row rises, or with fewer chance samples. None paid off.

rejectedrecorded
→
123456700weight on each next discall seven, each weighted 1/7five samples, two values unweighted
Fair expectimax

Seven chance samples instead of five

The next disc has seven equally likely values, but the reference samples only five. This tests all seven.

rejectedrecorded
→

Hand-built heuristics and shallow search

Scores visible board traits, adds them together, and plays the highest-valued column.

3 membersvoteevery column, every ply, three times
Hand-built heuristics and shallow search

Exact search

Three attempts add independent chance estimates, depth, and voters to a search. None repairs a biased board evaluation.

rejectedrecorded
→

No approach page matches that search.

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