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[2602.13700] Optimal Regret for Policy Optimization in Contextual Bandits
Summary
This paper presents a novel algorithm achieving optimal regret bounds for policy optimization in stochastic contextual multi-armed bandits, bridging theoretical and practical applications.
Why It Matters
Understanding optimal regret in policy optimization is crucial for improving decision-making processes in various applications, including recommendation systems and adaptive learning. This research provides a solid theoretical foundation that can enhance existing methods and lead to more efficient algorithms in real-world scenarios.
Key Takeaways
- Introduces the first high-probability optimal regret bound for policy optimization in contextual bandits.
- Algorithm achieves an optimal regret bound of $ ilde{O}( ext{sqrt}(K| ext{A}| ext{log}| ext{F}|))$.
- Results demonstrate the effectiveness of policy optimization methods in achieving rigorously-proven optimal performance.
- Empirical evaluations support the theoretical findings, showcasing practical applicability.
- Research bridges the gap between theoretical advancements and practical implementations in machine learning.
Computer Science > Machine Learning arXiv:2602.13700 (cs) [Submitted on 14 Feb 2026] Title:Optimal Regret for Policy Optimization in Contextual Bandits Authors:Orin Levy, Yishay Mansour View a PDF of the paper titled Optimal Regret for Policy Optimization in Contextual Bandits, by Orin Levy and Yishay Mansour View PDF HTML (experimental) Abstract:We present the first high-probability optimal regret bound for a policy optimization technique applied to the problem of stochastic contextual multi-armed bandit (CMAB) with general offline function approximation. Our algorithm is both efficient and achieves an optimal regret bound of $\widetilde{O}(\sqrt{ K|\mathcal{A}|\log|\mathcal{F}|})$, where $K$ is the number of rounds, $\mathcal{A}$ is the set of arms, and $\mathcal{F}$ is the function class used to approximate the losses. Our results bridge the gap between theory and practice, demonstrating that the widely used policy optimization methods for the contextual bandit problem can achieve a rigorously-proved optimal regret bound. We support our theoretical results with an empirical evaluation of our algorithm. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.13700 [cs.LG] (or arXiv:2602.13700v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.13700 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Orin Levy [view email] [v1] Sat, 14 Feb 2026 09:51:24 UTC (162 KB) Full-text links: Access Paper: View a PDF of ...