Afterstate learning
Distributional afterstate ranker
Measures every legal column under aligned futures and trains one network to rank the resulting positions.
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.
Afterstate learning
Judge the position a move leaves behind rather than the move itself, and build training data in which every column the player could have chosen has been measured, not guessed.
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.