Project Overview
This project implements an AI system that learns to play blackjack optimally using reinforcement learning while incorporating card counting strategies. The system adapts its betting and playing decisions based on the count of cards seen, demonstrating both basic strategy mastery and advanced gameplay techniques.
Core Features
- Q-learning implementation for strategy optimization
- Hi-Lo card counting system integration
- Dynamic betting strategy based on true count
- Multiple deck support (1-8 decks)
Performance Metrics
- Comprehensive performance tracking
- Win/loss/tie statistics
- ROI analysis across different deck counts
- Strategy deviation analysis
Technical Implementation
System Architecture
Game Engine
- Realistic blackjack rules implementation
- Deck management with shuffle tracking
- Dealer AI with house rules
- Multi-deck support with penetration tracking
AI Components
- Q-learning algorithm implementation
- Dynamic exploration/exploitation balance
- Card counting integration
- Adaptive betting strategy
AI Strategy
Basic Strategy
- Optimal play based on player/dealer cards
- Hard/soft hand differentiation
- Split and double-down decisions
- Progressive learning through gameplay
Advanced Techniques
- True count calculation
- Deck penetration tracking
- Betting spread optimization
- Strategy deviations based on count
Example Implementation
# Initialize AI with custom parameters
ai = BlackjackAI(
learning_rate=0.1,
discount_factor=0.95,
epsilon=0.1
)
# Run simulation
game = BlackjackGame(num_decks=6)
continue_game, reward = game.deal_initial_cards()
while continue_game:
state = game.get_game_state()
action = ai.choose_action(state)
continue_game, reward = game.player_action(action)
# Update AI's knowledge
new_state = game.get_game_state()
ai.update(state, action, reward, new_state)
Performance Analysis
Learning Metrics
- Win rate improvement over time
- Strategy convergence analysis
- Return on investment tracking
- Count-based decision accuracy
Optimization Results
- Deck number impact analysis
- Penetration depth effectiveness
- Betting spread optimization
- Risk-adjusted return metrics
Project Insights
Key Findings
- Optimal deck penetration for counting
- Impact of deck count on ROI
- Strategy deviation effectiveness
- Risk management insights
Future Enhancements
- Neural network integration
- Multi-player simulation support
- Advanced counting systems
- Real-time visualization tools