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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

1 approach of 87

Evolutionary optimisation

Tune a player's weights by playing complete games, keeping the settings that scored best, and repeating.

Read the primer →
depth 4leaf weights
Evolutionary optimisation

Evolving the leaf weights at the depth they are used

Evolves the reference evaluator on complete games, then tests the frozen winner once on unseen games.

→

No approach page matches that search.

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