a1aq/blackjack-Qtable

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.