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Breaking the spurious link: How causal models fix offline reinforcement learning's generalization problem
Researchers from Nanjing University and Carnegie Mellon University have introduced an AI approach that improves how machines learn from past data—a process known as offline reinforcement learning.
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More When you look at a baseball player hitting the ball, you can make ...
Alembic Technologies has raised $145 million in Series B and growth funding at a valuation 15 times higher than its previous round, betting that the next competitive advantage in artificial ...
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