Fair expectimax
Machine-tuning the board evaluator
Let an optimiser adjust eight coefficients of the search's board evaluator by playing complete games, then freeze the winner and test it on games it never saw.
rejectedrecordedApproaches
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.
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.
Hand-built heuristics and shallow search
Scores visible board traits, adds them together, and plays the highest-valued column.
Lifetime objective
Judge a move by how much longer the game will still last, rather than by how many points it scores right now.
Value and policy learning
Instead of searching ahead, train a model on past games to judge a board or pick a column, and learn why that kept failing.
No approach page matches that search.