Approaches

Constructive and reservoir policies

Build structures that pay off later and survive repeated row rises instead of taking only immediate points.

Constructive and reservoir policiesfeatured

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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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.

Fair expectimaxfeatured

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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Fair expectimaxfeatured

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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Fair expectimax

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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Hand-built heuristics and shallow search

Scores visible board traits, adds them together, and plays the highest-valued column.

Hand-built heuristics and shallow 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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Hand-built heuristics and shallow search

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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Lifetime objective

Judge a move by how much longer the game will still last, rather than by how many points it scores right now.

Lifetime objectivefeatured

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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Lifetime objectivefeatured

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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Lifetime objective

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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N-tuple networks and learning from play

Learn what makes a Drop7 board good from small cell patterns and millions of self-play games.

N-tuple networks and learning from play

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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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.

Value and policy 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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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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