Approaches

Expectimax search

Look a few moves ahead, take the best column on your own turns, and average over the discs the game might deal.

Fair expectimax reference (D3/D4)

The reference search looks four moves ahead, averages the sampled chance outcomes, and uses a hand-tuned board evaluator at the bottom.

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Expectimax searchfeatured

Separate reveal and next-disc sampling

Separates next-disc and hidden-value samples inside search. The change is worth about one extra move of look-ahead.

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Expectimax search

Sparse expectimax

Look several moves ahead, but instead of considering every disc the game might deal, take a small fixed handful of representative ones, and always finish the depth you promised.

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Expectimax search

Turning the reference search's constants into dials

Makes death penalty, search depth, and chance samples configurable to test which settings improve survival.

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Heuristic evaluation

Score a board with a hand-written sum of visible traits, then play the column whose board scores highest.

Read the primer →

Heuristic evaluationfeatured

The fair leaf evaluator

The hand-written board evaluator every search in this family uses at the bottom of its look-ahead, and the tuning bench its coefficients came from.

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Heuristic evaluation

Adding a rise-cycle residual to the evaluator

Adds a small rise-cycle term to board evaluation and preserves the fitted weights for later work.

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Heuristic evaluation

Tunneling

Dig a channel down beside the tall side of the board so that the dangerous gray discs high up can actually be reached and opened.

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Q-learning and value learning

Learn from past games how much each column is worth, so the player can rank moves without searching ahead.

Read the primer →

Q-learning and value learningfeatured

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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Q-learning and value 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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Q-learning and value 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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Q-learning and value 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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N-tuple networks

Learn the value of a board from small cell patterns, each with its own lookup table, trained over millions of self-play moves.

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N-tuple networks

Row and column lookup tables, learned from a billion moves

A lookup-table evaluator that reads every full row and full column of the board as one pattern, learns its numbers by temporal-difference play on the Rust engine, and replaces the hand-written leaf inside the depth-3 fair search.

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NNUE evaluators

A small neural network, cheap enough to run at every leaf of a search, that judges a board from its cells.

Read the primer →

NNUE evaluatorsfeatured

A learned survival estimate inside the reference search

Blends a learned survival estimate into the reference board evaluator and tests it under sampled and exact chance handling.

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Constructive planning

Plan a whole rise cycle toward a target board shape, so that later rises set off stacks prepared in advance.

Read the primer →

Constructive planningfeatured

Constructive spectrum

Plans a five-drop cycle toward a target board with spread-out heights, reachable covered discs, and shared triggers. It clearly beat the one-move-ahead reference.

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