Smith poker play in machine learning12/21/2023 ![]() ![]() What sets this model apart from previous AI poker systems is that the model uses far less domain knowledge than any prior poker AI model, the researchers explained. “ReBeL is effective in large-scale two-player zero-sum imperfect-information games and defeats a top human professional with statistical significance,” the researchers stated in their paper, Combining Deep Reinforcement Learning and Search for Imperfect-Information Games. ![]() It also needs to make complex predictions about what they cannot see in their opponents’ hand. Instead of just deciding on its next move, a reinforcement learning software agent playing Texas hold-em needs to make decisions based on all of the information it has seen. Facebook researchers developed a reinforcement learning model that can outmatch human competitors in heads-up, no-limit Texas hold’em, and turn endgame hold’em poker.Īt the heart of the model is how software-agents handle perfect-information games such as chess, versus imperfect-information games like poker. ![]()
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