Vertical reservoir

The most literal version of the family's idea: describe a good vertical structure directly, in features a person can name, and let an optimiser tune how much each one is worth. It ended up playing worse than the simple search it was meant to improve on.

The intuition

The family page shows the mechanism this approach is named after: a column of identical high discs that sits inert until its run reaches exactly the right length, then clears all at once. The bet here is that the shape of such a structure can be recognised directly from the board, without simulating the future at all.

So the evaluator is built out of named, deliberately non-linear quantities:

• release ladders — how close a stack is to being exactly releasable by one more disc, computed exactly rather than approximated;
• same-target powers — several discs sharing one target value, which is what makes a stack pay off all at once;
• escape wells — a low, empty column kept as a way out if the structure goes wrong;
• horizontal reachability and cover access — whether covered discs can still be attacked;
• low caps — the badness of a small number sitting on top of something you need.

None of this reads hidden information: it is a description of the visible board. The claim being tested is that structural potential is legible from a still image of the position.

How it works

1. Score every legal column by applying the evaluator to the position after the move, averaged over seven sampled chance outcomes; play the best.
2. Fit the evaluator's weights with a cross-entropy method — repeatedly sample a population of weight vectors, play complete games with each, keep the best few, and move the sampling distribution toward them.
3. Score every candidate on complete games played under common random numbers, so two candidates are always compared on the same games.
4. Freeze the winner and play a larger tournament against the one-move-ahead fair search.

What happened

It lost. On its 128-game tournament the frozen candidate averaged 160,498.1 points and 50.9 moves against fair D1's 178,554.4 and 55.8. Score, lifetime, lower-quartile score and reveal flow all failed admission, so the probe cohort was never opened.

The result is more interesting than a simple failure. The same family's constructive planner, which simulates a cycle forward instead of scoring a still image, beat the same comparator by a wide margin on the same kind of cohort. Taken together the two runs say something specific: it is not the reservoir idea that fails here, it is the attempt to read structural value off a static board. The research status makes the same point in general terms — similar-looking boards can have very different futures depending on how reachable triggers and covered discs evolve across the rises.

The technical record

Status: rejected — ledger-recorded. Experiment history §"Explicit reservoir and constructive-cycle policies (rejected)": 128-game tournament, candidate 160,498.094 points / 50.891 moves versus fair D1 178,554.438 / 55.750. Corrected 17,000-point scoring, checked as internally consistent by audit 03 row 4399. The ledger retains source and artifact SHA-256 values; the reproducibility guide notes that historical hashes no longer match current bytes after the tree was reorganised.

Frozen configuration, from the source: training seeds 0x3d630000–0x3d63ffff, tournament seeds 0x3d640000–0x3d64ffff, probe seeds 0x4d630000–0x4d63007f (never opened), 7 successor samples, 1,000-move cap, population 65, 13 elites, 24 generations, 64 games per fitting batch, 128 tournament games, 32 probe games, policy seed VRSV, 30-minute wall limit, 256 MiB RSS limit. Admission required at least 150 mean moves, at least 500,000 mean points and a 1.50 ratio on the fitting stage before the probe could open.

A related but separate experiment tested the same instinct inside the four-move search: a literal 7/6/5 vertical-ladder energy term screened cheaply at depth 2 looked promising and was rejected when transferred to depth 4. Both are listed in the experiment index under fair expectimax.

Source: vertical-reservoir-policy.cpp.

What this taught us, and what is still open

• A hand-named description of vertical structure, tuned directly on complete games, is not enough to beat even a one-move-ahead fair search.
• The failure is specific to reading structure from a still board. Simulating the cycle forward with the same underlying idea worked much better.
• This is a valid negative result on one frozen feature set. It does not rule out vertical structure as a component — but the one attempt to transfer a vertical-ladder term into the depth-4 reference also failed.