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--- |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# ConnectZero-Nakalipithecus |
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An AlphaZero-based Reinforcement Learning agent for Connect 4 game. |
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**Architecture:** ResNet (5 Residual Blocks) + Dual Head (Policy & Value). |
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**Framework:** PyTorch. |
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**Training Platform:** Kaggle T4 GPU. |
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**Author: Chakrabhuana Vishnu Deva.** |
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# Training result |
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``` |
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Total Parameter of the Model: 1,497,742 |
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Starting Training for 5 Iterations... |
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--- Iteration 1 --- |
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Self-Playing 100 games... |
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Data Collected: 1359 samples |
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Avg Loss: 2.9339 |
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--- Iteration 2 --- |
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Self-Playing 100 games... |
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Data Collected: 1644 samples |
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Avg Loss: 2.6747 |
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--- Iteration 3 --- |
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Self-Playing 100 games... |
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Data Collected: 1739 samples |
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Avg Loss: 2.4139 |
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--- Iteration 4 --- |
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Self-Playing 100 games... |
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Data Collected: 1678 samples |
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Avg Loss: 2.3377 |
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--- Iteration 5 --- |
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Self-Playing 100 games... |
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Data Collected: 2370 samples |
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Avg Loss: 2.1712 |
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Model Saved! |
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``` |
