Project Overview
This poker bot won 1st place in the UNC pokerbots 2025 competition by implementing sophisticated opponent modeling techniques and adaptive strategies.
Key Features
- Opponent Modeling: Dynamic adaptation to different playing styles
- Monte Carlo Simulations: Advanced probability calculations for optimal decision making
- Game Theory Optimal (GTO) Strategy: Balanced play to minimize exploitability
- Real-time Analytics: Live tracking of opponent patterns and betting behaviors
Technical Implementation
The bot uses machine learning algorithms to build models of opponent behavior, updating these models continuously throughout gameplay. Key components include:
- Bayesian inference for opponent type classification
- Regret minimization for strategy optimization
- Feature extraction from betting patterns
- Dynamic bankroll management
Results
- 1st Place in UNC pokerbots 2025 tournament
- 85% win rate against baseline opponents
- Successfully adapted to diverse playing styles during competition
What I Learned
This project taught me about game theory, machine learning in adversarial environments, and the importance of balancing exploitation with exploration in competitive scenarios.