arxiv
PublishedJune 3, 2026 at 4:00 AM
—neutral
Minimax Optimal Strategy for Delayed Observations in Online Reinforcement Learning
Publisher summary· verbatim
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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Originally published on arxiv ↗