arXiv Machine Learning
Nonpreemptive Scheduling While Learning Context-Dependent Service Rates
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arXiv Machine Learning
Jun 9
Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret
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.
arXiv Machine Learning
Jul 14
Learning to Schedule in Parallel-Server Queues with Stochastic Bilinear Rewards
arXiv:2112. 06362v5 Announce Type: replace Abstract: We consider the problem of scheduling in multi-class, parallel-server queuing systems with uncertain rewards from job-server assignments.
arXiv Machine Learning
Jul 23
Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence
arXiv:2607. 19854v1 Announce Type: new Abstract: We study horizon-free regret minimization for finite-horizon time-homogeneous tabular Markov decision processes with $S$ states, $A$ actions, horizon $H$, and per-trajectory total reward bounded by $1$.
arXiv Machine Learning
Jun 29
Learning in Markovian bandits with non-observable states and constrained decision epochs
arXiv:2606. 27448v1 Announce Type: new Abstract: This paper studies the problem of regret minimization in Markovian bandits with \emph{non-observable states} and possibly \emph{constrained} decision epochs.
arXiv Statistics ML
Sep 2
Model Predictive Control is almost Optimal for Heterogeneous Restless Multi-armed Bandits
arXiv:2511.08097v2 Announce Type: replace-cross Abstract: We consider a general infinite horizon Heterogeneous Restless multi-armed Bandit (RMAB). Heterogeneity is a fundamental problem for many real...
arXiv Machine Learning
Jul 28
Finite-Time Analysis of the Natural Policy Gradient in Finite-Horizon Markov Decision Processes
arXiv:2607. 22982v1 Announce Type: new Abstract: Natural Policy Gradient (NPG) is a well-established Reinforcement Learning algorithm that underlies widely used methods such as Trust Region Policy Optimization and Proximal Policy Optimization, both of which have demonstrated strong empirical success.