Poker AI - Optimal Strategy 2.4.6

The Texas Holdem Poker game has become a popular subject in Game Theory and Artificial Intelligence research. Similar to other renowned games like Chess and Go, poker has been analyzed using algorithms such as Tree Search, Reinforcement Learning, and Neural Networks. However, the multiplayer no-limit Texas Holdem presents a more complex challenge due to its vast number of possible states and the presence of hidden information.

Our AI solution is primarily based on the Counterfactual Regret Minimization (CFR) algorithm, which is widely used in poker AI. Instead of using the more popular Monte Carlo CFR variant, we have further improved our CFR algorithm by implementing a Dynamic Programming approach. This is made possible by conducting separate sampling and training stages. By pre-processing the data through sampling and abstraction techniques, the resulting tree becomes small enough to be solved entirely by CFR, making computational feasibility achievable through DP methods.

Our application features a powerful and efficient AI engine, combined with a user-friendly UI environment. It offers multiple configurations that allow users to customize their own poker game, catering to both training and leisure purposes.

For more information, please feel free to contact us at: sanqystudio@foxmail.com

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