Gespeichert in:
| 1. Verfasser: | |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2603.26547 |
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Inhaltsangabe:
- We adapt the analysis of policy gradient for continuous time $k$-armed stochastic bandits by Lattimore (2026) to the standard discrete time setup. As in continuous time, we prove that with learning rate $η= O(Δ_{\min}^2/(Δ_{\max} \log(n)))$ the regret is $O(k \log(k) \log(n) / η)$ where $n$ is the horizon and $Δ_{\min}$ and $Δ_{\max}$ are the minimum and maximum gaps.