Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees
arXiv:2606.09002v1 Announce Type: cross Abstract: We study a stochastic multi-armed bandit problem in which the set of available arms expands over time. This setting arises in sequential experimentation when new actions or treatments become available during an ongoing study, making regret against a single best arm in hindsight inappropriate. We instead evaluate performance relative to the best arm currently available, leading to a dynamic-regret criterion for arriving-arm environments. To address the resulting challenges of arrival information discrepancy (AID) and a drifting benchmark (DB), w
This research addresses fundamental challenges in multi-armed bandit problems with an expanding set of options, a growing reality in dynamic AI and experimentation environments.
Improving the efficiency of sequential experimentation and decision-making in evolving systems is crucial for optimizing AI agent performance and resource allocation in complex, real-world scenarios.
The development of new theoretical frameworks for dynamic regret and sublinear guarantees allows for more robust and adaptable AI systems in environments where new choices emerge over time.
- · AI agents developers
- · Reinforcement learning researchers
- · Experimentation platforms
- · Adaptive algorithm designers
- · Static optimization approaches
- · Systems unprepared for dynamically expanding choice sets
New algorithms and methodologies emerge for sequential decision-making under uncertainty with evolving options.
AI systems become more adept at continuously integrating novel opportunities and adapting to expanding solution spaces.
Industries reliant on dynamic resource allocation and rapid innovation see accelerated development and optimized operational efficiency.
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