<ul><li>Researchers have proposed a method called selective uncertainty propagation for confidence interval construction in offline reinforcement learning.</li><li>The method is designed to address the challenges of estimating treatment effects and dealing with distributional shifts in real-world RL instances.</li><li>Selective uncertainty propagation adapts to the level of difficulty associated with distribution shift challenges.</li><li>The technique has shown promising results in toy environments and is beneficial for offline policy learning.</li></ul>

Selective Uncertainty Propagation in Offline RL

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