Approaches

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 playfeatured

Fixing the learning rule, and conditioning on the rise clock

Fixes an n-tuple update bug and adds rise-cycle context. The corrected learner remains too weak.

N-tuple networks and learning from playfeatured

N-tuple value with a two-rise look-ahead

The family's largest run trained a pattern evaluator on 50 million moves and searched two row rises deep. Search made it worse than trusting the network alone.

rejectedrecorded

N-tuple networks and learning from play

Blending the learned value with the hand-written one

Mix the learned board value with the hand-written heuristic and see whether the combination beats either alone. It did not.

rejectedrecorded

N-tuple networks and learning from play

Learning from every column, not just the one played

An n-tuple value learner whose every update looks at all seven legal columns under the same imagined luck, so that exploration noise cannot leak into what it learns.

rejected

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.

completed