arXiv:2606.09668v1 Announce Type: new Abstract: Contextual queueing bandits provide a framework for learning to schedule heterogeneous jobs under unknown context-dependent service rates. Under stochastic contexts, existing algorithms achieve $\widetilde{\mathcal{O}}(T^{-1/4})$ queue length regret, defined as the expected difference between the learner's and oracle's queue lengths at horizon $T$. In this paper, we improve this rate to $\widetilde{\mathcal{O}}(T^{-1/2})$. The key observation is that random exploration is needed only up to a carefully chosen cutoff round, rather than throughout t
The paper provides a significant algorithmic improvement in the field of contextual queueing bandits, crucial for systems with dynamic resource allocation under uncertainty.
Improving the queue length regret from O(T^-1/4) to O(T^-1/2) directly translates to more efficient and reliable autonomous scheduling systems, impacting numerous applications.
This algorithmic advancement allows for more robust and resource-optimizing AI agents and automated decision-making systems in dynamic environments.
- · AI/ML researchers and developers
- · Logistics and supply chain companies
- · Cloud computing providers
- · Telecommunications companies
- · Inefficient scheduling algorithms
- · Systems relying on heuristic resource allocation
Improved performance and resource utilization in systems using contextual queueing bandits.
Faster adoption and deployment of autonomous AI agents in complex operational settings due to enhanced efficiency.
Increased automation across industries, leading to productivity gains and potential shifts in labor requirements for operational management.
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