The paper introduces a new approach to learning chance-constrained Markov decision processes (CCMDPs) using a Bellman distributional certificate. It provides both model-based and model-free algorithms with theoretical guarantees, including matching upper and lower bounds for tabular discounted CCMDPs with bounded successor support. Numerical experiments on synthetic CCMDPs and an IEEE 14-bus energy storage benchmark demonstrate the safety and effectiveness of the proposed methods.
By Chenbei Lu, Hongyu Yi
The tutorial titled "Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers" explores how modern deep learning techniques—such as neural networks, transformers, large language models, and deep reinforcement learning—can be integrated with operations research and management science to address complex, uncertain, and dynamic decision problems. It argues that deep learning should complement, not replace, optimization, offering adaptability and scalable approximation while OR/MS provides rigorous constraint and uncertainty modeling. The tutorial organizes the field around predict‑then‑optimize, decision‑aware learning, constraint‑aware decision generation, and deep reinforcement learning, and highlights applications across supply chains, healthcare, energy, and autonomous systems.
By I. Esra Buyuktahtakin
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
arXiv:2607. 14373v1 Announce Type: new Abstract: We propose a noise-robust elicit-to-optimize framework that integrates inverse reinforcement learning (IRL) and reinforcement learning (RL) for eliciting agents' risk preferences and optimizing policies under a broad class of risk objectives characterized by distortion riskmetrics.
By Yang Liu, Yuhao Liu, Yunran Wei
arXiv:2607. 06610v1 Announce Type: cross Abstract: Portfolio optimization under uncertainty is inherently a multi-objective decision problem involving complex interactions among return, risk, market dynamics, and practical investment constraints.
By Sounaq Das, Tanmay Sen, Raghu Nandan Sengupta, Aditya Gupta
arXiv:2608. 03562v1 Announce Type: new Abstract: Reinforcement learning (RL) with general utility extends classic RL by optimizing an arbitrary utility functional of the policy-induced occupancy measure, thereby enabling a broader range of applications.
By Zixuan Liu, Fangzheng Wu, Brian Summa, Zizhan Zheng