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About this paper
Seoungbin Bae, Dabeen Lee
This research from KAIST and Seoul National University develops an algorithm for contextual queueing bandits that achieves a $\tilde{O}(T^{-1/2})$ queue length regret rate, improving upon previous $\tilde{O}(T^{-1/4})$ bounds. The work establishes that this rate is minimax-optimal by proving a matching $\Omega(T^{-1/2})$ lower bound, precisely characterizing the problem's inherent learning limits.
Key insights
- Queue length regret, as a terminal-horizon measure, permits a faster convergence rate than previously thought, achieving $\tilde{O}(T^{-1/2})$ rather than $\tilde{O}(T^{-1/4})$.
- Strategic, phased management of exploration and exploitation, rather than continuous random exploration, can lead to superior regret rates for terminal performance objectives.
- The problem's inherent difficulty, measured by the minimax lower bound, aligns with the $\Omega(T^{-1/2})$ rate, demonstrating the algorithm's optimality up to logarithmic factors.
Summary by alphaXiv.
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