Create README.md

README.md

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+"Tic Tac Toe Tricks
+There are several distinct strategies that can be employed to ensure victory when playing tic tac toe, but there are also a few simple tricks that new players can use to help their chances.
+Remember, this game is known as a 'solved game', which means that there is a definite strategy that can be employed to win every single time. However, if both players employ that same unbeatable strategy, the game will result in a draw every time."
+https://www.siammandalay.com/2021/05/18/how-to-win-tic-tac-toe-tricks-to-always-win-noughts-crosses/
+
+
+model initialized on cpu.
+ReplayBuffer initialized with capacity: 50000
+ReplayBuffer initialized with capacity: 50000
+Loaded 12 seed examples for player X (after augmentation if any) into ReplayBuffer.
+Loaded 12 seed examples for player O (after augmentation if any) into ReplayBuffer.
+Loaded 8 seed examples for player X (after augmentation if any) into ReplayBuffer.
+Loaded 8 seed examples for player O (after augmentation if any) into ReplayBuffer.
+PygameDisplay initialized.
+GameLogger initialized. Logging to: ttt_runs_output_optimized\run_optimized_v1.0_20250605_091852\game_logs, Images to: ttt_runs_output_optimized\run_optimized_v1.0_20250605_091852\image_frames
+
+--- Evaluating models after game 100 ---
+Evaluation (50 games): X Wins: 0, O Wins: 50, Draws: 0. X Win Rate: 0.00
+
+--- Evaluating models after game 200 ---
+Evaluation (50 games): X Wins: 0, O Wins: 50, Draws: 0. X Win Rate: 0.00
+
+--- Evaluating models after game 300 ---
+Evaluation (50 games): X Wins: 0, O Wins: 50, Draws: 0. X Win Rate: 0.00
+
+--- Evaluating models after game 400 ---
+Evaluation (50 games): X Wins: 0, O Wins: 50, Draws: 0. X Win Rate: 0.00
+
+--- Starting Game 500/10000 ---
+LRs: X=1.0e-04, O=1.0e-04. Buffers: X=1655, O=1751
+Training after game 500: Avg Loss X: 0.6662, Avg Loss O: 0.9334
+
+--- Evaluating models after game 500 ---
+Evaluation (50 games): X Wins: 25, O Wins: 25, Draws: 0. X Win Rate: 0.50
+
+--- Evaluating models after game 600 ---
+Evaluation (50 games): X Wins: 25, O Wins: 25, Draws: 0. X Win Rate: 0.50
+
+--- Evaluating models after game 700 ---
+Evaluation (50 games): X Wins: 25, O Wins: 25, Draws: 0. X Win Rate: 0.50
+
+--- Evaluating models after game 800 ---
+Evaluation (50 games): X Wins: 25, O Wins: 25, Draws: 0. X Win Rate: 0.50
+
+--- Evaluating models after game 900 ---
+Evaluation (50 games): X Wins: 0, O Wins: 25, Draws: 25. X Win Rate: 0.00
+
+--- Starting Game 1000/10000 ---
+LRs: X=1.0e-04, O=1.0e-04. Buffers: X=3333, O=3516
+Training after game 1000: Avg Loss X: 0.5366, Avg Loss O: 0.7208
+
+--- Evaluating models after game 1000 ---
+Evaluation (50 games): X Wins: 0, O Wins: 25, Draws: 25. X Win Rate: 0.00
+
+--- Evaluating models after game 1100 ---
+Evaluation (50 games): X Wins: 0, O Wins: 25, Draws: 25. X Win Rate: 0.00
+
+--- Evaluating models after game 1200 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 1300 ---
+Evaluation (50 games): X Wins: 0, O Wins: 25, Draws: 25. X Win Rate: 0.00
+
+--- Evaluating models after game 1400 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Starting Game 1500/10000 ---
+LRs: X=1.0e-04, O=1.0e-04. Buffers: X=5099, O=5332
+Training after game 1500: Avg Loss X: 0.4487, Avg Loss O: 0.6971
+
+--- Evaluating models after game 1500 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 1600 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 1700 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 1800 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 1900 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Starting Game 2000/10000 ---
+LRs: X=1.0e-04, O=1.0e-04. Buffers: X=6989, O=7211
+Training after game 2000: Avg Loss X: 0.4598, Avg Loss O: 0.5945
+
+--- Evaluating models after game 2000 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 2100 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 2200 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 2300 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 2400 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Starting Game 2500/10000 ---
+LRs: X=1.0e-04, O=1.0e-04. Buffers: X=8869, O=9108
+Training after game 2500: Avg Loss X: 0.4708, Avg Loss O: 0.5109
+
+--- Evaluating models after game 2500 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 2600 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 2700 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 2800 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 2900 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Starting Game 3000/10000 ---
+LRs: X=1.0e-04, O=1.0e-04. Buffers: X=10761, O=10998
+Training after game 3000: Avg Loss X: 0.4827, Avg Loss O: 0.5137
+
+--- Evaluating models after game 3000 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 3100 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 3200 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 3300 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 3400 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Starting Game 3500/10000 ---
+LRs: X=1.0e-04, O=1.0e-04. Buffers: X=12651, O=12881
+Training after game 3500: Avg Loss X: 0.3450, Avg Loss O: 0.4849
+
+--- Evaluating models after game 3500 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 3600 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 3700 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 3800 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 3900 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Starting Game 4000/10000 ---
+LRs: X=1.0e-04, O=1.0e-04. Buffers: X=14521, O=14750
+Training after game 4000: Avg Loss X: 0.4175, Avg Loss O: 0.5450
+
+--- Evaluating models after game 4000 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00
+
+--- Evaluating models after game 4100 ---
+Evaluation (50 games): X Wins: 0, O Wins: 0, Draws: 50. X Win Rate: 0.00