Datasets:

LKForge
/

2048-solver-benchmark

license: mit
pretty_name: 2048 Solver Benchmark (expectimax depth sweep)
language:
  - en
tags:
  - '2048'
  - expectimax
  - game-ai
  - search-depth
  - benchmark
task_categories:
  - other
size_categories:
  - n<1K
configs:
  - config_name: depth_sweep
    data_files: data/2048-benchmark.json

2048 Solver Benchmark — Expectimax Depth Sweep

By LK Forge: privacy-first browser tools & games, nothing to install, nothing uploaded.

How well the LK Forge 2048 Solver plays as a function of search depth. The shipped engine is an expectimax search with a corner-snake heuristic, plus probability-threshold pruning and a transposition table (the pruning/table approach follows nneonneo's canonical 2048-ai). Self-played headlessly at fixed depths 1–6 plus the shipped adaptive setting.

How the solver works: https://lkforge.com/games/2048/blog-how-the-ai-solver-works.html
Full write-up: https://lkforge.com/blog/2048-expectimax-depth-benchmark/
Try it: https://lkforge.com/games/2048/
Producer: LK Forge — client-side AI games, solvers, and tools.

Headline numbers

Below a 3-ply lookahead the solver never reaches 2048 — 0% of games at depth 1 and depth 2.
Depth 3 is the threshold: reach-2048 jumps to 66%, and the mean score jumps six-fold (from ~4,400 to ~29,600) for that one extra ply.
Quality climbs to 97% reach at depth 5, but a depth-6 move costs about 83× more compute than depth 3 for a shrinking gain.
The shipped adaptive engine reaches 2048 in 94% of 300 games (depth 4 on an open board, rising to depth 7 when it is nearly full), pushes to 4096 in 70%, and breaks 8192 in 9%, at 1.8 ms/move. A separate 250-game run of the same engine measured 92.8% reach-2048 — corroborating within sampling error.

Files

data/2048-benchmark.json — per-depth reach-rates (1024/2048/4096/8192), mean/median score, and mean ms/move for fixed depths 1–6, plus the adaptive run and a headline summary.

Method

Engine: the exact shipped engine (public/games/2048/solver-core.js), self-played headlessly — the numbers describe the search the game actually runs.
Reach-rate = share of games that reached that tile at least once.
Spawns: standard 90% "2" / 10% "4" tile spawns.
Samples: depths 1–4 and adaptive are 300 games each; depth 5 is 100 games, depth 6 is 30 games (each move there is far slower), so those percentages carry wider error bars.
Tile spawns use a seeded PRNG so the run is re-runnable. The search itself is deterministic — it expands every empty cell rather than sampling — so all game-to-game variance comes from the random spawns, and reach-rate is a distribution, not a single fixed value.
Timing is mean milliseconds per move on one laptop. Measured 23 September 2026 (reworked engine).

Reproduce

node scripts/benchmark-2048-depth.mjs
# or: GAMES=300,300,300,300,100,30 DEPTHS=1,2,3,4,5,6 node scripts/benchmark-2048-depth.mjs

The same engine is published as a standalone, MIT-licensed repo that reproduces the adaptive run:

git clone https://github.com/lucian-devops/2048-ai-solver && cd 2048-ai-solver
node benchmark.js 250   # ~20 minutes: ~2 ms/move, ~3,000 moves per game

Citation

LK Forge (2026). 2048 Solver Benchmark — Expectimax Depth Sweep. https://lkforge.com/blog/2048-expectimax-depth-benchmark/

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