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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

5 approaches of 87

Risk and survival objectives

Judge a move by its worst outcomes, or by how long the game keeps going, and see whether caution extends life.

Read the primer →
refusedlower boundfour membersfallbackfloor
Risk and survival objectivesfeatured

Conservative fitted policy iteration

Learn the whole spread of how long a game might last, and only leave the safe move when the evidence says so.

rejectedrecorded
→
worst 25%rootoutcomesweightthe worst outcomes weigh more
Risk and survival objectivesfeatured

Weighting the worst outcomes at the root

Instead of judging a column by its average outcome, judge it partly by its worst outcomes, and find that the resulting policy dies much sooner.

rejectedrecorded
→
52641inout2.4 clears + 1.4 reveals per movethe board fills anyway
Risk and survival objectives

Gray throughput

Clear numbered discs and open covered ones fast enough to keep the board from filling.

rejected
→
playedcol 2col 5meanworst quarter
Risk and survival objectives

Risk sensitive

Ranks columns partly by their worst imagined futures. Every tested risk setting trails the baseline.

rejected
→
resampleelitesmilestonesmoves livedtuned for survival, not the mean
Risk and survival objectives

Tail-survival CEM

Retunes an existing policy for survival milestones instead of average score while changing nothing else. The result barely moved.

rejectedrecorded
→

No approach page matches that search.

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