arXiv Machine Learning

Nonpreemptive Scheduling While Learning Context-Dependent Service Rates

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.

By Seoungbin Bae, Dabeen Lee

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.

By Jung-hun Kim, Milan Vojnovic

reinforcement-learning efficiency

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 $1$.

By Runlong Zhou, Zihan Zhang, Maryam Fazel, Simon S. Du

reinforcement-learning

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.

By Thomas Hira, Victor Boone, Urtzi Ayesta, Ina Maria Verloop

reinforcement-learning benchmarks safety

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...

By Dheeraj Narasimha, Nicolas Gast

reinforcement-learning

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.

By Asha Barua, Sajad Khodadadian

reinforcement-learning

arXiv Machine Learning

Sep 15

High-Probability Nash Regret for Decentralized Learning in Markov $\alpha$-Potential Games: Episodic and Fully Online Asynchronous Algorithms with Applications to Markov Congestion Games

arXiv:2609. 14959v1 Announce Type: new Abstract: We study decentralized learning of Nash equilibria (NE) in infinite-horizon discounted Markov games under bandit feedback, focusing on Markov $\alpha$-potential games.

By S. Rasoul Etesami

reinforcement-learning safety

arXiv Machine Learning

1d ago

Fine-Tuning on Self-Generated and Reward-Weighted Data: Learning Dynamics, Convergence Rates, and Benefits of Off-Policyness

arXiv:2609.36945v1 Announce Type: new Abstract: We study the learning dynamics of fine-tuning a policy model on self-generated and reward-weighted data, with particular focus on a generalized version...

By Zhiwei Wang, Yanxi Chen, Yaliang Li, Bolin Ding

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 2

Online Learning in MDPs with Partially Adversarial Transitions and Losses

arXiv:2602. 09474v2 Announce Type: replace Abstract: We study reinforcement learning in MDPs whose transition function is stochastic at most steps but may behave adversarially at a fixed subset of $\Lambda$ steps per episode.

By Ofir Schlisselberg, Tal Lancewicki, Yishay Mansour

reinforcement-learning safety

arXiv Machine Learning

Jun 25

Minimax PAC Bounds for Learning in Exogenous Contextual MDPs

arXiv:2606. 25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.

By Corentin Pla, Hugo Richard, Marc Abeille, Vianney Perchet

agents reinforcement-learning

arXiv Machine Learning

Aug 11

Information Routing across Batch Boundaries: Memory--Batch Tradeoffs in Lipschitz Bandits

arXiv:2608. 07922v1 Announce Type: new Abstract: Adaptive learning needs both a state that preserves what observations imply and opportunities to act on that state.

By Zicheng Lyu, Zengfeng Huang

arXiv Machine Learning

Jul 24

Exploiting Exogenous Structure for Sample-Efficient Reinforcement Learning

arXiv:2409. 14557v4 Announce Type: replace-cross Abstract: We study a structured class of Markov Decision Processes, known as Exo-MDPs, in which the state space is partitioned into exogenous and endogenous components.

By Jia Wan, Sean R. Sinclair, Devavrat Shah, Martin J. Wainwright

agents reinforcement-learning

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