---
title: TypeScript MCTS lab
family: tree-search
summary: A small Monte Carlo tree search you can run in a terminal and watch one decision at a time.
status: rejected
evidence: task-record only
reads: public
---

A small, readable Monte Carlo tree search you can run in a terminal: the one
place in the repository where the idea is easy to watch.

<EvidenceLabel status="rejected" evidence="task-record only" reads="public" />

<Callout title="There is no retained result for this program" tone="warn">
The [experiment index](/docs/research/experiment-index) records this source as
rejected because "ordinary MCTS did not establish a whole-game improvement",
and labels that verdict **task-record only** — meaning it comes from a research
conversation, not from the written ledger. There is no protocol, no artifact,
no per-game data and no score to quote.
[Audit 04](/docs/exploratory/audit-04-blind-spots) lists this source among the
thirty rejections that "cannot be re-derived". Everything below the outcome
section therefore describes what the code does, from the code.
</Callout>

## What it is

The C++ programs in this family are research instruments: thousands of lines,
frozen gates, sanitizer builds. This one is a few hundred lines of TypeScript
that plays complete games and prints a line of statistics. It exists so the
idea can be read, modified and run without a compiler, and because the solver
it drives is shared with the
[benchmark playground](/leaderboard).

## How it works, step by step

The search itself lives in `src/core/typescript/mcts-solver.ts` and is called
by the lab in a loop until the game ends.

1. The lab starts a game from a seed and hands the solver the public position:
   board, visible next disc, moves until the next rise, terminal flag. The
   solver is given its own fixed random stream, which is deliberately *not* the
   game's seed, so the search cannot sample the future the game will actually
   deal.
2. Each simulation walks down the stored tree, choosing at every step the
   column with the best average result plus an exploration bonus for the
   least-visited ones. Tree nodes are keyed by the complete visible position,
   so a branch reached by two different routes is the same node, and a
   decision made after a sampled reveal is conditioned on the board that was
   actually observed, not on a future decided in advance.
3. When the walk reaches a position the tree has not stored, the simulation is
   finished by a short greedy playout and the resulting board is scored by the
   heuristic evaluator. Dying is charged a large fixed penalty.
4. Results are added back along the path. After the last simulation the lab
   plays the root column with the best average, the real game deals a real
   disc, and the loop repeats.

The stored tree has a hard entry limit; once it is full, further simulations
fall back to the bounded playout evaluator rather than growing without bound.

## Defaults, and what they mean

| Setting | Lab default | What it controls |
| --- | ---: | --- |
| `--simulations` | 2,000 | How many futures are sampled per move |
| `--horizon` | 20 | How many moves ahead a simulation may run |
| `--rollout-depth` | 2 | Greedy moves used to finish a new position |
| `--exploration` | 40,000 | Bonus for under-visited columns, in score units |
| `--max-nodes` | 100,000 | Hard cap on stored positions |
| `--terminal-utility` | −1,000,000 | The penalty charged for dying |
| `--games` / `--max-moves` | 4 / 1,000 | Size of a run |

The registered playground policy uses a much smaller budget — 400 simulations
and a sixteen-move horizon, so that a scripted round finishes quickly. Those
rounds are a demonstration and are never used as research evidence.

## What happened

Nothing was retained. The conclusion carried forward from this program is the
one sentence in the index: ordinary Monte Carlo tree search did not establish a
whole-game improvement over the reference. That statement is consistent with
the three ledger-recorded C++ experiments in this family, which failed their
frozen gates for reasons that were diagnosed in detail, but it is not itself
evidence, and it should not be cited as though it were. If this program is ever
run against fair depth 4 on a paired cohort with a registered protocol, the
result will be the first real measurement this file has.

## What this taught us, and what is still open

- **Provenance is part of a result.** A rejection with no protocol and no data
  cannot be checked, reproduced, or rescored when the rules change, and the
  scoring rules did change here, from a 7,000-point rise bonus to 17,000.
- **The cheapest open experiment in this family** is probably not a new idea
  but a registered run of code that already exists: this lab, and the
  [PUCT lab](/approaches/tree-search/puct) next door, both complete and both
  without a retained outcome.