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The University of Tokyo
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Centering advantages in policy gradients can drastically reduce variance and improve performance in reinforcement learning tasks.
OrderGrad transforms policy-gradient optimization by enabling precise control over distributional properties, allowing for risk-averse and exploratory learning in real-world applications.
