Use Reinforcement Learning to Turn AI Agents Into Continuous Learners - NVIDIA
Use Reinforcement Learning to Turn AI Agents Into Continuous Learners NVIDIA
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Google DeepMind s Research Lets an LLM Rewrite Its Own Game Theory Algorithms — And It Outperformed the Experts
Designing algorithms for Multi-Agent Reinforcement Learning (MARL) in imperfect-information games — scenarios where players act sequentially and cannot see each other s private information, like poker — has historically relied on manual iteration. Researchers identify weighting schemes, discounting rules, and equilibrium solvers through intuition and trial-and-error. Google DeepMind researchers proposes AlphaEvolve, an LLM-powered evolutionary coding agent [ ] The post Google DeepMind s Research Lets an LLM Rewrite Its Own Game Theory Algorithms — And It Outperformed the Experts appeared first on MarkTechPost .
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