owlsrcool2011 / tictac

Experimenting with different techniques for playing tic-tac-toe

Demo project for different approaches for playing tic-tac-toe.

Code requires python 3, numpy, and pytest. For the neural network/dqn implementation (qneural.py), pytorch is required.

Create virtual environment using pipenv:

• pipenv --site-packages

Install using pipenv:

• pipenv shell
• pipenv install --dev

Set PYTHONPATH to main project directory:

• In windows, run path.bat
• In bash run source path.sh

Run tests and demo:

• Run tests: pytest
• Run demo: python -m tictac.main
• Run neural net demo: python -m tictac.main_qneural

Latest results:

C:\Dev\python\tictac>python -m tictac.main
Playing random vs random:
-------------------------
x wins: 60.10%
o wins: 28.90%
draw  : 11.00%

Playing minimax not random vs minimax random:
---------------------------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing minimax random vs minimax not random:
---------------------------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing minimax not random vs minimax not random:
-------------------------------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing minimax random vs minimax random:
-----------------------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing minimax random vs random:
---------------------------------
x wins: 96.80%
o wins: 0.00%
draw  : 3.20%

Playing random vs minimax random:
---------------------------------
x wins: 0.00%
o wins: 78.10%
draw  : 21.90%

Training qtable X vs. random...
700/7000 games, using epsilon=0.6...
1400/7000 games, using epsilon=0.5...
2100/7000 games, using epsilon=0.4...
2800/7000 games, using epsilon=0.30000000000000004...
3500/7000 games, using epsilon=0.20000000000000004...
4200/7000 games, using epsilon=0.10000000000000003...
4900/7000 games, using epsilon=2.7755575615628914e-17...
5600/7000 games, using epsilon=0...
6300/7000 games, using epsilon=0...
7000/7000 games, using epsilon=0...
Training qtable O vs. random...
700/7000 games, using epsilon=0.6...
1400/7000 games, using epsilon=0.5...
2100/7000 games, using epsilon=0.4...
2800/7000 games, using epsilon=0.30000000000000004...
3500/7000 games, using epsilon=0.20000000000000004...
4200/7000 games, using epsilon=0.10000000000000003...
4900/7000 games, using epsilon=2.7755575615628914e-17...
5600/7000 games, using epsilon=0...
6300/7000 games, using epsilon=0...
7000/7000 games, using epsilon=0...

Playing qtable vs random:
-------------------------
x wins: 93.10%
o wins: 0.00%
draw  : 6.90%

Playing qtable vs minimax random:
---------------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing qtable vs minimax:
--------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing random vs qtable:
-------------------------
x wins: 0.00%
o wins: 61.00%
draw  : 39.00%

Playing minimax random vs qtable:
---------------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing minimax vs qtable:
--------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing qtable vs qtable:
-------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

number of items in qtable = 747

Training MCTS...
400/4000 playouts...
800/4000 playouts...
1200/4000 playouts...
1600/4000 playouts...
2000/4000 playouts...
2400/4000 playouts...
2800/4000 playouts...
3200/4000 playouts...
3600/4000 playouts...
4000/4000 playouts...

Playing random vs MCTS:
-----------------------
x wins: 0.00%
o wins: 63.50%
draw  : 36.50%

Playing minimax vs MCTS:
------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing minimax random vs MCTS:
-------------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing MCTS vs random:
-----------------------
x wins: 81.80%
o wins: 0.00%
draw  : 18.20%

Playing MCTS vs minimax:
------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing MCTS vs minimax random:
-------------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing MCTS vs MCTS:
---------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

C:\Dev\python\tictac>python -m tictac.main_qneural
Training qlearning X vs. random...
200000/2000000 games, using epsilon=0.6...
400000/2000000 games, using epsilon=0.5...
600000/2000000 games, using epsilon=0.4...
800000/2000000 games, using epsilon=0.30000000000000004...
1000000/2000000 games, using epsilon=0.20000000000000004...
1200000/2000000 games, using epsilon=0.10000000000000003...
1400000/2000000 games, using epsilon=2.7755575615628914e-17...
1600000/2000000 games, using epsilon=0...
1800000/2000000 games, using epsilon=0...
2000000/2000000 games, using epsilon=0...
Training qlearning O vs. random...
200000/2000000 games, using epsilon=0.6...
400000/2000000 games, using epsilon=0.5...
600000/2000000 games, using epsilon=0.4...
800000/2000000 games, using epsilon=0.30000000000000004...
1000000/2000000 games, using epsilon=0.20000000000000004...
1200000/2000000 games, using epsilon=0.10000000000000003...
1400000/2000000 games, using epsilon=2.7755575615628914e-17...
1600000/2000000 games, using epsilon=0...
1800000/2000000 games, using epsilon=0...
2000000/2000000 games, using epsilon=0...

Playing qneural vs random:
--------------------------
x wins: 98.40%
o wins: 0.00%
draw  : 1.60%

Playing qneural vs minimax random:
----------------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing qneural vs minimax:
---------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing random vs qneural:
--------------------------
x wins: 0.00%
o wins: 75.40%
draw  : 24.60%

Playing minimax random vs qneural:
----------------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing minimax vs qneural:
---------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%

Playing qneural vs qneural:
---------------------------
x wins: 0.00%
o wins: 0.00%
draw  : 100.00%