Constructive and reservoir policies
Panel value
Builds complete training data by playing every legal column through the same imagined futures, then learns from it. Both models still ranked columns worse than the four-move search.
rejectedApproaches
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.
Constructive and reservoir policies
Build structures that pay off later and survive repeated row rises instead of taking only immediate points.
Depth-4 search and long outcomes
Judges moves over 25-step continuations, then tries to retain only choices that survive the longer view.
N-tuple networks and learning from play
Learn what makes a Drop7 board good from small cell patterns and millions of self-play games.
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.