Explore Once, Evaluate Many: Multiple Policy Evaluation for Stochastic Linear Bandits
We study how to evaluate $K$ target policies simultaneously in stochastic contextual bandits with linear reward structure. Given a known $d$-dimensional feature map and reward-free access to policy queries, we propose PRISMA (Policy evaluation through Regularized Information Sharing and Mixture Allocation), an algorithm that estimates every target policy's value to accuracy $\varepsilon$ with probability at least $1-δ$. The algorithm uses $\widetilde{\mathcal{O}}\!\left( \frac{(L^2S^2+β^2)(1\veeρ^\star)}{\varepsilon^2} \cdot \log(K) \right)$ context draws and reward observations, where $ρ^\star\le d$ is an instance-dependent measure of the difficulty of designing a shared sampling distrib...
| Authors | Spyros Dragazis, Yilei Chen, Aldo Pacchiano |
|---|---|
| Primary category | cs.LG |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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