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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

Afterstates

Score the board a move leaves behind, and build training data in which every column the player could have chosen was measured.

Read the primer →
4663753214513262546513645afterstaterises aheadhow long, not how much
Afterstates

A network that predicts how long you will live

Plans a neural evaluator that predicts remaining row rises so search can favor survival over immediate points.

→
3333333seven afterstatesquantilesone number eachranked by the whole distribution
Afterstates

Distributional afterstate ranker

Measures every legal column under aligned futures and trains one network to rank the resulting positions.

rejectedrecorded
→
6623267124351625364251644444443614one tapeone rootseven siblingsthe gap, not the value
Afterstates

Sibling advantage

Stop asking how good a board is. Replay all seven columns through exactly the same imagined future and learn only which one came out ahead.

rejected
→

No approach page matches that search.

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