Paper · arXiv
A Diffusion Analysis of Policy Gradient for Stochastic Bandits
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 $η= O(Δ^2)$.
| Authors | Tor Lattimore |
|---|---|
| Primary category | stat.ML |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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