Poker AI AgentSource-available · no public deploy
[ Overview ]
A poker application featuring AI opponents driven by Counterfactual Regret Minimization (CFR). The agent traverses the game tree to compute regret for each action — fold, call, raise — and refines its strategy over time to minimise exploitability in an imperfect-information game. A real-time opponent-modelling system adapts to player aggression and bluffing frequency, and an event-driven socket layer keeps human and AI game state in sync.
[ Engineering highlights ]
- CFR strategy that approximates Nash equilibrium
- Adaptive opponent modelling for aggression & bluffs
- Bluff detection and execution
- Real-time multiplayer state synchronisation
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