arXiv Machine Learning July 28, 2026 By Young Hyun Cho, Franz Stoll, Will Wei Sun, Guang Lin, Stephan Biller

Two-Timescale Hierarchical Reinforcement Learning for Resilient Operations

Read the original on arXiv Machine Learning →

arXiv:2607. 23434v1 Announce Type: cross Abstract: Unexpected shocks recur in global operations, requiring decision rules that adapt as market and operating conditions change.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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

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arXiv Machine Learning

Aug 11

Learning Multi-Timescale Interventions under Safety and Resource Constraints

arXiv:2508. 03875v2 Announce Type: replace Abstract: Many sequential decision problems offer qualitatively different ways of influencing the environment: some interventions act immediately, whereas others induce persistent effects that continue to shape future states long after the decision that initiated them.

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arXiv Machine Learning

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Resource-Adaptive Primal-Dual Learning for One-Warehouse Multi-Store Systems with Censored Demand

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By Jiameng Lyu

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arXiv AI

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

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By Amandeep Kaur, Gyan Prakash

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arXiv AI

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Maturing Markov Decision Processes: Decision Making under Increasing Information and Shrinking Action Sets

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By Jiaxi Liu, Aiping Yang, Yuhang Yang, Shuqi Zhang, Zewei Dong, Jiangming Yang, Xuebin Chen

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arXiv Machine Learning

Aug 4

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

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

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Related stories

Exploiting Exogenous Structure for Sample-Efficient Reinforcement Learning

Learning Multi-Timescale Interventions under Safety and Resource Constraints

Resource-Adaptive Primal-Dual Learning for One-Warehouse Multi-Store Systems with Censored Demand

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

Maturing Markov Decision Processes: Decision Making under Increasing Information and Shrinking Action Sets

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