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News/Minimax Optimal Strategy for Delayed Observations in Online Reinforcement Learning
arxiv
PublishedJune 3, 2026 at 4:00 AM
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Minimax Optimal Strategy for Delayed Observations in Online Reinforcement Learning

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arXiv:2603.03480v2 Announce Type: replace Abstract: We study reinforcement learning with delayed state observation, where the agent observes the current state after some random number of time steps. We propose an algorithm that combines the augmentation method and the upper confidence bound approach

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