Tree-Guided Identify-Then-Exploit: A Unified Framework of Best Arm Identification and Regret Minimization for Dueling Bandits
arXiv:2606.01799v1 Announce Type: new Abstract: We study $N$-armed stochastic dueling bandits under the Condorcet-winner assumption, where three widely adopted objectives are considered: best-arm identification (BAI), weak regret, and strong regret. We propose Tree-Guided Identify-Then-Exploit (TG-ITE), the first unified framework to tackle all these objectives to our knowledge. Without requiring stronger assumptions, we propose a shared tree-guided identification approach to find a high-confidence incumbent within $O(N)$ comparisons. We further propose varied exploitation strategies to utiliz
This research introduces a novel, unified framework for reinforcement learning problems, advancing the state-of-the-art in autonomous decision-making algorithms.
Improved and generalized algorithms for dueling bandits have direct implications for the efficiency and robustness of AI agents operating in complex environments, such as those related to recommendation systems or automated negotiation.
The ability to tackle multiple objectives (best-arm identification, weak, and strong regret) within a single framework simplifies the development of more versatile and robust AI systems.
- · AI researchers
- · AI software developers
- · Companies utilizing AI for decision making
- · Developers using less efficient, fragmented approaches
More efficient and reliable AI agents will become possible due to this unified framework.
This improved efficiency could accelerate the deployment and adoption of AI systems in various industries.
As AI agents become more capable and ubiquitous, they could further automate complex tasks, impacting white-collar workflows.
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