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LICENSE.txt

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+The MIT License (MIT)
+
+Copyright (c) 2014 Gabriele Cirulli
+Copyright (c) 2026 Yeth (yeth.dev)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+1. The above copyright notice and this permission notice shall be included in
+   all copies or substantial portions of the Software.
+
+2. Credit must be given to Yeth as the original
+   author of the AI model, training code, and dataset in any derivative work,
+   publication, or distribution that uses them.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

README.md

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+---
+license: mit
+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).

config.json

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+{
+  "architecture": "n-tuple-network",
+  "num_patterns": 8,
+  "tuple_size": 6,
+  "num_symmetries": 8,
+  "num_values": 15,
+  "table_size": 11390625,
+  "board_size": 4,
+  "tile_encoding": "log2 (0=empty, 1=2, 2=4, ... 14=16384)",
+  "learning_algorithm": "TD(0) afterstate",
+  "games_played": 1200000,
+  "max_tile": 16384,
+  "weight_dtype": "float32",
+  "weight_byte_order": "little-endian",
+  "total_weight_bytes": 364500000,
+  "symmetries": [
+    "identity",
+    "flip-x",
+    "flip-y",
+    "flip-xy",
+    "transpose",
+    "transpose-flip-x",
+    "transpose-flip-y",
+    "transpose-flip-xy"
+  ]
+}

patterns.json

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+[
+  [
+    [
+      0,
+      0
+    ],
+    [
+      1,
+      0
+    ],
+    [
+      2,
+      0
+    ],
+    [
+      3,
+      0
+    ],
+    [
+      0,
+      1
+    ],
+    [
+      1,
+      1
+    ]
+  ],
+  [
+    [
+      0,
+      0
+    ],
+    [
+      1,
+      0
+    ],
+    [
+      0,
+      1
+    ],
+    [
+      1,
+      1
+    ],
+    [
+      0,
+      2
+    ],
+    [
+      1,
+      2
+    ]
+  ],
+  [
+    [
+      0,
+      0
+    ],
+    [
+      1,
+      0
+    ],
+    [
+      2,
+      0
+    ],
+    [
+      0,
+      1
+    ],
+    [
+      1,
+      1
+    ],
+    [
+      2,
+      1
+    ]
+  ],
+  [
+    [
+      0,
+      0
+    ],
+    [
+      1,
+      0
+    ],
+    [
+      0,
+      1
+    ],
+    [
+      1,
+      1
+    ],
+    [
+      2,
+      1
+    ],
+    [
+      3,
+      1
+    ]
+  ],
+  [
+    [
+      0,
+      0
+    ],
+    [
+      1,
+      0
+    ],
+    [
+      2,
+      0
+    ],
+    [
+      3,
+      0
+    ],
+    [
+      2,
+      1
+    ],
+    [
+      3,
+      1
+    ]
+  ],
+  [
+    [
+      0,
+      0
+    ],
+    [
+      1,
+      0
+    ],
+    [
+      2,
+      0
+    ],
+    [
+      1,
+      1
+    ],
+    [
+      2,
+      1
+    ],
+    [
+      3,
+      1
+    ]
+  ],
+  [
+    [
+      0,
+      0
+    ],
+    [
+      1,
+      0
+    ],
+    [
+      0,
+      1
+    ],
+    [
+      1,
+      1
+    ],
+    [
+      2,
+      1
+    ],
+    [
+      2,
+      2
+    ]
+  ],
+  [
+    [
+      0,
+      0
+    ],
+    [
+      1,
+      0
+    ],
+    [
+      2,
+      0
+    ],
+    [
+      0,
+      1
+    ],
+    [
+      1,
+      1
+    ],
+    [
+      1,
+      2
+    ]
+  ]
+]

weights.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:10dc981c51c646d271fdffba796557e79c7d8c892456ccf3140ec3e4b85866aa
+size 364500000