---
license: other
license_name: mit-attribution
tags:
  - "2048"
  - reinforcement-learning
  - n-tuple-network
  - game-ai
language:
  - en
---

# 2048 N-Tuple Network Model

Trained using TD(0) afterstate learning with 8 six-tuple patterns and 8 symmetries.

## Stats

- Games trained: 1,200,000
- Max tile reached: 16384
- Patterns: 8 six-tuples with 8 symmetry transforms each
- Weight table size: ~347 MB

## Files

- `weights.bin` - raw Float32 weight tables (8 x 11390625 floats)
- `config.json` - model architecture and training metadata
- `patterns.json` - tuple pattern definitions

## Usage

Load the binary weights into 8 Float32Arrays of size 11390625 each.
For each board state, compute the feature index for each pattern under all 8 symmetries
and sum the corresponding weight values to get the board evaluation score.
Pick the move whose afterstate has the highest (reward + evaluation).