arXiv Machine Learning October 27, 2026 By Baris Ata, Yufan Chen, Robert Montgomery

A Dynamic Inventory Rationing Model for Last-Mile Delivery of Medication in Liberia's Community Health Program

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arXiv Machine Learning

Aug 6

Differentiating Through Dual Prices: End-to-End Policy Learning Under Capacity Constraints

arXiv:2608. 04669v1 Announce Type: new Abstract: Many social services assign scarce resources, such as housing assistance or hospital interventions, to people who arrive one at a time: each arrival must receive a decision immediately, and the long-run usage of every resource must stay within its capacity.

By Mohammadsaeed Haghi, Mahdi Salmani, Nima Kelidari

arXiv AI

1d ago

Learning a patient world model for health system simulation in intensive care

arXiv:2611.08392v1 Announce Type: new Abstract: Designing healthcare strategies requires anticipating their clinical and operational consequences before implementation, a task that often relies on he...

By Yingtai Li, Ruoyan Wang, Yu Li, Shaohua Kevin Zhou

arXiv Machine Learning

Jun 2

Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach

arXiv:2503. 24183v3 Announce Type: replace Abstract: The expansion of ride-sourcing services such as Uber and Lyft has reshaped urban transportation by offering flexible, on-demand mobility via mobile applications.

By Matej Jusup, Kenan Zhang, Zhiyuan Hu, Barna P\'asztor, Andreas Krause, Francesco Corman

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arXiv AI

Jun 6

Learning to replenish: A hybrid deep reinforcement learning for dynamic inventory management in the pharmaceutical supply chains

arXiv:2606. 06201v1 Announce Type: new Abstract: Pharmaceutical supply chains (PSCs) struggle with inventory management (IM) due to unpredictable demand patterns and variable lead times associated with restocking.

By Amandeep Kaur, Gyan Prakash

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arXiv Machine Learning

Aug 4

Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection

arXiv:2608. 02343v1 Announce Type: cross Abstract: Many operational problems are constrained sequential decision processes with large, combinatorial action spaces and interdependent feasibility constraints.

By Patrick Helm, Jan-Niklas Doerr, Joren Gijsbrechts, Stefan Minner

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arXiv Machine Learning

Sep 30

Fair Policy Optimization in Major-Minor Weakly Coupled Markov Decision Processes

arXiv:2609.36174v1 Announce Type: new Abstract: We consider fair resource allocation in sequential decision-making environments modeled as major-minor weakly coupled Markov decision processes (M2WCMD...

By Xiaohui Tu, Yossiri Adulyasak, Erick Delage

reinforcement-learning safety

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Differentiating Through Dual Prices: End-to-End Policy Learning Under Capacity Constraints

Learning a patient world model for health system simulation in intensive care

Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach

Learning to replenish: A hybrid deep reinforcement learning for dynamic inventory management in the pharmaceutical supply chains

Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection

Fair Policy Optimization in Major-Minor Weakly Coupled Markov Decision Processes