fqjin / ultimate-tictactoe

Selfplay reinforcement learning for ultimate tic-tac-toe (UTTT)

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Selfplay reinforcement learning
for Ultimate Tic-Tac-Toe (UTTT)

Run gui.py to play against a neural network.

Rules:

  • The game board consists of a 3x3 grid of mini-boards, each having 3x3 tiles.
  • Player X begins by playing in any tile. Play then alternates between players O and X.
  • Players must play in the mini-board corresponding to the location of the previous move in its respective mini-board. For example, if the previous move was an X in the upper-right corner of the lower-right miniboard, then player O must play in the upper-right miniboard.
  • A mini-board is filled when it is won by a player (3 tiles in a row) or all tiles are taken (tie). When sent to a filled mini-board, a player can play in any of the unfilled mini-boards.
  • The game is won when a player wins the big board (3 mini-boards in a row). Tied mini-boards do not count for either player. The game is tied if neither player can win the big board.

Selfplay strength estimate

Game Viewer

Use viewer.py to view and analyze UTTT games.

About

Selfplay reinforcement learning for ultimate tic-tac-toe (UTTT)

License:GNU General Public License v3.0


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