arXiv:2510.12152v2 Announce Type: replace-cross Abstract: We study the decoupled multi-armed bandit problem, where the learner separately selects one arm for exploration and one, possibly different, arm for exploitation at each round. In this setting, the loss of the explored arm is observed but not incurred, whereas the loss of the exploited arm is incurred without being observed. We propose an efficient Follow-the-Perturbed-Leader (FTPL) policy that achieves Best-of-Both-Worlds (BOBW) guarantee with constant regret in the stochastic regime and optimal $O(\sqrt{KT})$ regret in the adversarial
The paper represents continuous advancement in the theoretical understanding and practical application of AI, specifically in designing more efficient and robust learning algorithms for complex decision-making scenarios.
Improved bandit algorithms enhance the efficiency of AI systems facing exploration-exploitation trade-offs, leading to faster learning and better performance in areas such as personalized recommendations, clinical trials, and resource allocation.
This research introduces a novel Follow-the-Perturbed-Leader (FTPL) policy for decoupled multi-armed bandits, offering 'Best-of-Both-Worlds' performance, thus providing more stable and optimal learning outcomes across varying environments.
- · AI researchers
- · Machine learning platform developers
- · Companies utilizing A/B testing and personalization
- · Sectors with dynamic resource allocation challenges
- · Inefficient reinforcement learning algorithms
- · Systems reliant on sub-optimal bandit policies
More efficient and adaptive AI-driven decision-making systems will emerge across various industries.
Enhanced algorithmic performance could accelerate the development and deployment of more sophisticated AI agents in real-world applications.
The widespread adoption of such robust learning systems might lead to more optimized societal resource distribution and personalized services, impacting economic efficiency and individual experiences.
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