README.md

metadata

license: mit
version: 1.0.0
language:
  - en
pretty_name: Tic-Tac-Toe Distinct Legal Move Dataset
size_categories:
  - 1M<n<10M
tags:
  - tic-tac-toe
  - board-games
  - game-dataset
  - ai
  - ml
  - reinforcement-learning
  - supervised-learning
  - q-learning
  - minimax
  - explainable-ai
  - symmetry
  - dataset-generation
  - python
  - educational
task_categories:
  - reinforcement-learning
  - tabular-classification
  - tabular-regression
  - other

🧠 Tic-Tac-Toe Distinct Legal Move Dataset

📘 Overview

This dataset contains all possible legal Tic-Tac-Toe game sequences — that is, every unique path through the complete game tree (about 255,168 distinct sequences) from start to finish.

Each row represents one move in one possible game, with metadata like:

• Which player made the move
• The board state before the move
• The move position
• The final game result (Win/Loss/Draw)
• A symmetry_id that groups equivalent boards under rotations and reflections

This dataset can be used for machine learning experiments, game theory analysis, or reinforcement learning training.

---

🧩 Dataset Structure

Column	Description
game_id	Unique identifier for each full game sequence
step	Step number within the game (1–9)
player	X or O — the player who made the move
board_state	The board represented as a 9-element list (1 = X, -1 = O, 0 = Empty)
next_move	The index (0–8) of the next move in the flattened 3×3 grid
result	The final outcome of the game (X Win, O Win, or Draw)
board_state_str	Human-readable board string (e.g., X,_,_,O,_,_,_,_,_)
symmetry_id	A hash of the canonical form of the board after accounting for rotations/reflections

---

🔍 Example

game_id	step	player	board_state	next_move	result	board_state_str	symmetry_id
1	1	X	[0,0,0,0,0,0,0,0,0]	0	X Win	_,_,_,_,_,_,_,_,_	812347823
1	2	O	[1,0,0,0,0,0,0,0,0]	4	X Win	X,_,_,_,_,_,_,_,_	982734987
1	3	X	[1,0,0,0,-1,0,0,0,0]	8	X Win	X,_,_,_,O,_,_,_,_	123459812

---

🧮 Data Generation Logic

The dataset was programmatically generated by exploring the complete Tic-Tac-Toe game tree using recursive backtracking in Python.

Key features:

• Symmetry normalization: Boards that are identical under rotation/reflection are reduced to a single canonical form.
• Comprehensive coverage: Every legal sequence is included.
• Direct serialization: Each move is written as a CSV row for efficient analysis.