Upload Blackjack Q-table model

README.md

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+# Blackjack Q-table Policy
+
+This repository contains the trained tabular Q-learning policy used in the IEMS5726 Blackjack Reinforcement Learning Trainer project.
+
+## Model
+
+- 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
+
+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 |
+
+`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
+
+## Usage
+
+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`.

q_learning_model_comparison.csv

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+model,average_reward,win_rate,loss_rate,draw_rate,eval_episodes
+best_basic_count,-0.008698,0.433611,0.481673,0.084716,1000000
+five_m_expert_prior,-0.008698,0.433611,0.481673,0.084716,1000000
+five_m_aggressive,-0.024801,0.430806,0.482306,0.086888,1000000

training_history.csv

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+episode,epsilon,average_reward,win_rate,loss_rate,draw_rate