Minimax Optimal Strategy for Delayed Observations in Online Reinforcement Learning
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. For tabular Markov decision processes (MDPs), we derive a regret bound of $\tilde{\mathcal{O}}(H \sqrt{D_{\max} SAK})$, where $S$ and $A$ are the cardinalities of the state and action spaces, $H$ is the time horizon, $K$ is the number of episodes, and $D_{\max}$ is the maximum length of the delay. We also provide a matching lower bound up to logarithmic factors, showing the optimality of our approach. Our analytical framework formulates this p...
| Authors | Harin Lee, Kevin Jamieson |
|---|---|
| Primary category | cs.LG |
| Published | 3 March 2026 |
| Last updated at source | 2 June 2026 |
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