| --- |
| tags: |
| - reinforcement-learning |
| - blackjack |
| - q-learning |
| - tabular |
| license: mit |
| --- |
| |
| # Blackjack Q-table Policy |
|
|
| This repository contains the trained tabular Q-learning policy used in the IEMS5726 Blackjack Reinforcement Learning Trainer project. |
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| ## Model |
|
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| - Algorithm: tabular Q-learning |
| - Policy artifact: `q_model.json` |
| - Actions: `0 = stand`, `1 = hit`, `2 = double_down` |
| - State representation: `(player_total, dealer_upcard, usable_ace, can_double, true_count_bucket)` |
| - Rules: one player versus dealer, finite 6-deck shoe, Hi-Lo running count, true-count bucket, double down, dealer stands on soft 17, natural Blackjack pays 3:2. |
|
|
| ## Evaluation |
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| The selected policy was evaluated over 1,000,000 Blackjack hands. |
|
|
| | Metric | Value | |
| | --- | ---: | |
| | Average reward | -0.0086975 | |
| | Win rate | 0.433611 | |
| | Loss rate | 0.481673 | |
| | Draw rate | 0.084716 | |
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| `q_learning_model_comparison.csv` compares the selected expert-prior/count policy against 5,000,000-hand fine-tuning variants. |
|
|
| ## Files |
|
|
| - `q_model.json`: trained Q-table policy and evaluation metadata |
| - `policy_table.csv`: exported policy table |
| - `policy_heatmap.svg`: policy visualization |
| - `training_history.csv`: training-history file generated by the training pipeline |
| - `q_learning_model_comparison.csv`: final model comparison table |
|
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| ## Usage |
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| The browser demo can load this model JSON directly from the public URL. In the project submission, place the public link in `application/model_link.txt`. |
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|
