---
title: Observable-state MCTS
family: tree-search
summary: Grow the look-ahead by simulation, but only ever let the search know what a player would actually be able to see.
status: rejected
evidence: ledger-recorded
reads: public
---
Grow the look-ahead by simulation, but only ever let the search know what a
player would actually be able to see.
<EvidenceLabel status="rejected" evidence="ledger-recorded" reads="public" />
## The problem this was built to solve
There is a cheap way to plan under uncertainty that does not work. Guess the
whole future in advance: every disc the game will deal, every hidden gray
number it will show. Then plan perfectly inside that imagined future, and
average the results over many guesses. Each individual plan looks brilliant,
because each was made by a planner who already knew the answers. Averaging
brilliant plans that could never have been made by the same player produces a
number that flatters the first move and cannot be achieved by anybody. The
ledger calls this failure *strategy fusion*; the glossary entry is
[determinization](/learn/glossary).
This approach removes the opportunity. A position in its tree is identified by
the visible board, the visible next disc, the moves left until the next rise,
whether the game is over, and how much search horizon remains, and by nothing
else. No game seed, no future disc sequence, no hidden number is stored
anywhere in a node or passed to any decision. A chance outcome is drawn only
when the search actually crosses that event for the first time, and later
decisions along that line see only the visible position that resulted. Because
one policy has to work across all the futures, it cannot specialize to any of
them.
## How it works, step by step
1. Read the public position: board, next disc, rise clock, terminal flag.
2. Run many simulations. Each one walks down the stored tree preferring
columns with good recorded results and a bonus for the least-tried ones,
adds one new position where the walk leaves the tree, finishes the line with
a cheap one-move-ahead fair search out to a fixed horizon, and credits every
column choice on the path with the result.
3. Draw chance outcomes lazily. The first few times an action edge is crossed,
sample a fresh outcome and keep it in a small per-edge pool; afterwards,
replay from that pool. The original lab caps the pool at eight outcomes.
4. Play the column with the best record at the root — or, in the gated
variant, hand the decision back to exact fair search unless the tree's
preference clears a fixed confidence rule.
Three programs live here, and it is worth reading them as one argument rather
than three experiments: the first asked whether the idea ranks moves at all,
the second asked whether it can be used safely as an override on top of a
search that already works, and the third asked whether it was simply too small.
## What happened
All three were retired, and the third is the one worth remembering.
The plain search **almost** cleared its bar. On positions it had never seen, it
ranked pairs of columns slightly better than the exact depth-3 comparator and
had substantially lower regret, but its rate of picking the single best column
was 11 correct out of 32, and the frozen gate required a rate that 11 out of 32
just misses. Nothing was retuned and no further games were opened, which is the
correct behaviour and also why there is no better number to report.
The gated version, allowed to override a working depth-3 search only when it
was confident, cut its disagreements from 34 positions to 12 and did improve
its top-choice rate, but it got *worse* at ranking pairs and its regret rose.
A rule that switches rarely and is wrong when it switches is worse than no rule.
The scaled version is the informative failure. It was given four times the
simulations, twice the horizon, twice the chance pool, and a proper evaluator
at the end of each playout. Against short-horizon reference values it improved
enormously. It became an excellent imitator of the four-move reference. Against
what actually mattered, the outcome twenty-five moves later, it got worse than
the small version it replaced. The audit's conclusion was blunt: the remaining
failure is not search quantity, and this candidate should not receive a
gameplay screen. The cheap one-move-ahead playout policy and the small replayed
pool of chance outcomes were optimizing the wrong future, and a bigger budget
buys more of the wrong future.
None of these three ever played a cohort of complete games. Everything below is
a measurement of how well a search ranks the columns available at stored
positions, against labels made by playing those positions forward.
<TechnicalDetails title="The technical record">
**Observable-state stochastic UCT** — rejected at the held-out gate,
ledger-recorded. Tuning used 64 roots from 32 origin games with independent
32-scenario labels and 60-move public depth-1 continuations, over all twelve
combinations of 256/1,024/4,096/16,384 simulations and horizons 8/16/32; the
preregistered minimum-regret rule froze 16,384 simulations and horizon 32. On
32 roots from 16 disjoint games it reached 0.6498 pairwise accuracy and 28,420
mean regret versus exact depth 3's 0.6418 and 44,142, but top-1 accuracy was
11/32 = 0.34375 against a 0.35 gate. The screen and confirmation seeds were not
read and nothing was retuned. Maximum active storage was 7,876,784 bytes
against a fixed 8,257,960-byte reservation, with at most 15,486 nodes.
**Confidence-gated MCTS over fair depth 3** — rejected, ledger-recorded.
Fitting used 64 roots from 32 origin games, each sibling evaluated on 64
aligned 80-move public fair-depth-1 continuations. The frozen thresholds were a
Q margin of 3.2 and a visit-share margin of 0.8. Raw MCTS disagreed with fair
depth 3 on 34 of 64 fitting roots and the gate reduced that to 12, but fitting
regret was still worse than fair (29,783.74 versus 25,848.30). On the one
allowed held-out set (32 roots from 16 origin-disjoint games) the gate
switched six actions: tie-aware top-1 rose from 0.28125 to 0.34375, pairwise
accuracy fell from 0.58742 to 0.54755, and regret rose from 28,165.14 to
32,443.13. Raw MCTS alone was weaker still at 0.21875, 0.51994 and 41,105.89.
**Scaled public-state observable MCTS** — rejected before gameplay,
ledger-recorded. A ranking-only audit that opened no gameplay seed and
reconstructed no origin game; it replayed 36 already-stored public roots (one
fixed middle root from each of 24 fitting and 12 disjoint held-out games) and
recomputed fair depth-3 and depth-4 values under corrected 17,000-point
Hardcore scoring. The candidate was frozen before any ranking at 65,536
simulations, horizon 64, a 16-outcome reservoir, the same depth-1 playout and
one public fair leaf at a surviving cutoff.
On the 12 held-out roots, against corrected 25-move labels:
| Predictor | Top-1 | Pairwise | Normalized regret |
| --- | ---: | ---: | ---: |
| Old observable MCTS | 25.00% | 61.02% | 0.29450 |
| Scaled candidate | 25.00% | 56.78% | 0.37251 |
| Exact public fair D3 | 25.00% | 57.63% | 0.35712 |
| Exact public fair D4 | 33.33% | 55.93% | 0.36314 |
Both six-game held-out halves lost pairwise accuracy and the second more than
doubled normalized regret, from 0.13592 to 0.29193, so the frozen gate failed
and no fresh, protected, or final seed was opened. The diagnostic contrast is
the point of the entry: measured against corrected fair-D4 root values on the
same states, scaling raised top-1 from 66.67% to 91.67%, pairwise from 66.10%
to 74.58%, and cut normalized regret from 0.03896 to 0.00356. The audit ran in
550.296 seconds within a 47,710,856-byte fixed arena.
**Limitations.** The held-out sets are 32 and 12 roots. Both are small,
and the 12-root table separates predictors by a single root in places.
`observable-mcts-lab.cpp` and `fair-mcts-confidence.cpp` carry a compile-time
assertion pinning the historical 7,000-point Sequence level bonus, so their
recorded numbers are archival and are not comparable with corrected Hardcore
results; only the scaled audit was recomputed at 17,000 points. All three are
root-ranking measurements, not whole-game scores.
Sources: "Observable-state stochastic UCT (rejected at held-out gate)",
"Confidence-gated MCTS over fair depth 3 (rejected)", and "Scaled public-state
observable MCTS (rejected before gameplay)" in the
[experiment history](/docs/research/history), and the tree-search table in the
[experiment index](/docs/research/experiment-index).
</TechnicalDetails>
## What this taught us, and what is still open
- **A sampling search can preserve the information boundary.**
That is a real engineering result: nodes keyed only by the visible position,
self-tests for determinism, reflection, metadata blindness and legality, and
hard arena bounds that were never exhausted.
- **Ranking well against a short-horizon teacher is not the same as ranking
well against the future.** The scaled audit measured both at once and they
moved in opposite directions. This is the sharpest version of the lesson on
the [sibling ranking](/learn/concepts/ranking-siblings) page.
- **What was ruled out** is this configuration: a myopic one-move-ahead playout
with a small replayed chance pool, at any budget tested. What was *not* ruled
out is Monte Carlo tree search in this game. The ledger's own recommendation
is a separately frozen long-value or playout replacement, and unbiased
sampling of fresh chance events, not another budget increase.