Play, learn, solve, and analyze No-Limit Texas Hold Em. Implementation follows from Monte Carlo counter-factual regret minimization over with hierarchical K-means imperfect recall abstractions.

A simplified solver written for Heads Up No-Limit Hold'Em Poker

River Nash equilibrium solver with bet size solving functionality

This project is a heads-up no-limit Texas Hold’em solver designed to compute GTO strategies. It leverages CFR with efficient abstractions, allowing scalable strategy computation and analysis. The solver supports range input, action abstraction, and real-time decision-making, making it suitable for both research and practical gameplay analysis

A GTO solver for Spin & Go poker variant

Robust Deep Monte Carlo Counterfactual Regret Minimization: Addressing Theoretical Risks in Neural Fictitious Self-Play

A Monte Carlo CFR solver for All-In or Fold poker, computing approximate Nash-equilibrium strategies efficiently