Poker Hand

Poker, a game of imperfect information, serves as a crucial benchmark for artificial intelligence research, particularly in developing agents capable of strategic decision-making under uncertainty. Current research focuses on improving AI performance in various poker variants using techniques like deep reinforcement learning (with architectures such as residual networks), counterfactual regret minimization (CFR), and large language models (LLMs). These advancements are not only pushing the boundaries of AI capabilities but also informing the development of more robust algorithms applicable to other complex decision-making problems in diverse fields.