GTO Solver

Counterfactual Regret Minimization

Compute game-theoretically optimal (GTO) strategies using the CFR algorithm. This approximates Nash equilibrium play through iterative regret minimization.

CFR Configuration
Set up the game state and training parameters
50%

Probability of winning the hand at showdown

5,000

More iterations = better convergence (5,000-10,000 recommended)

Scenario Summary
Your Equity:50%
Current Pot:100 chips
Bet to Call:50 chips
Total Pot if Called:150 chips
Pot Odds:33.3%
GTO Strategy
Optimal strategy distribution will appear here
Run CFR simulation to compute strategy

CFR Algorithm Explanation

Counterfactual Regret Minimization (CFR) is an iterative algorithm that computes approximate Nash equilibrium strategies in imperfect information games. The algorithm tracks "regret" for not taking each action and adjusts strategy probabilities to minimize cumulative regret over many iterations. The resulting mixed strategy prevents exploitation by opponents who know your tendencies. This implementation is simplified for academic research and demonstrates the core CFR concepts.