Neural Networks in Poker
Noam discusses the complexity of decision-making in poker, highlighting how neural networks can help generalize strategies from past experiences to navigate new situations. By leveraging similar past scenarios, players can interpolate their decisions, ultimately guiding them toward a Nash equilibrium. The introduction of deep counterfactual regret minimization allows for efficient summarization of strategies, eliminating the need to reference every previous game iteration.In this clip
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The Robot Brains Podcast
S3 E14 OpenAI Research Scientist Noam Brown on Solving Poker and Diplomacy with AI
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