A Diffusion Analysis of 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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| _version_ | 1866918381881393152 |
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| author | Lattimore, Tor |
| author_facet | Lattimore, Tor |
| contents | We study a continuous-time diffusion approximation of policy gradient for $k$-armed stochastic bandits. We prove that with a learning rate $η= O(Δ^2/\log(n))$ the regret is $O(k \log(k) \log(n) / η)$ where $n$ is the horizon and $Δ$ the minimum gap. Moreover, we construct an instance with only logarithmically many arms for which the regret is linear unless $η= O(Δ^2)$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10219 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Diffusion Analysis of Policy Gradient for Stochastic Bandits Lattimore, Tor Machine Learning Artificial Intelligence Statistics Theory We study a continuous-time diffusion approximation of policy gradient for $k$-armed stochastic bandits. We prove that with a learning rate $η= O(Δ^2/\log(n))$ the regret is $O(k \log(k) \log(n) / η)$ where $n$ is the horizon and $Δ$ the minimum gap. Moreover, we construct an instance with only logarithmically many arms for which the regret is linear unless $η= O(Δ^2)$. |
| title | A Diffusion Analysis of Policy Gradient for Stochastic Bandits |
| topic | Machine Learning Artificial Intelligence Statistics Theory |
| url | https://arxiv.org/abs/2603.10219 |