Approaches

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.

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Denoised public value

Instead of asking one future what a board was worth, simulate 32 futures from it and average them.

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Value and policy learningfeatured

Klein-Friedmann linear Q-learning

Score each column with six hand-made numbers about what the drop does right now, learn the six weights from experience, and find out how much of Drop7 that can see.

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Value and policy learning

Double-DQN and continuation

Use textbook reinforcement learning to score each column from experience, reward survival, and add a short look-ahead.

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Value and policy learning

One-ply Q as a pruning prior and a refit leaf

Fit a linear action value to the depth-4 search's own sibling values, then use it not to play but to decide which siblings the search may skip, and separately refit the leaf to the search's own value.

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