A Lyapunov Analysis of Softmax Policy Gradient for Stochastic Bandits
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912985239257088 |
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| author | Lattimore, Tor |
| author_facet | Lattimore, Tor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26547 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Lyapunov Analysis of Softmax Policy Gradient for Stochastic Bandits Lattimore, Tor Machine Learning 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. |
| title | A Lyapunov Analysis of Softmax Policy Gradient for Stochastic Bandits |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.26547 |