arXiv Machine Learning

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

arXiv AI

Jun 17

Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning

arXiv:2606. 18111v1 Announce Type: cross Abstract: Fairness is an important aspect of decision-making in multi-objective reinforcement learning (MORL), where policies must ensure both optimality and equity across multiple, potentially conflicting objectives.

By Umer Siddique, Peilang Li, Yongcan Cao

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 10

Trading Utility for Dynamic Fairness in Multiple Resource Division with Sequential Demand

arXiv:2606. 10472v1 Announce Type: cross Abstract: Dynamic multi-resource allocation is a central problem in shared computing environments, where users' demands arrive sequentially and resources must be distributed fairly without knowledge of future demands.

By Kaiqi Jiang, Karim El Husseini, Wenzhe Fan, Xinhua Zhang

safety

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

agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Aug 3

End-to-End Fairness Optimization with Fair Decision-Focused Learning

arXiv:2607. 29441v1 Announce Type: new Abstract: Many real-world systems rely on predictive models to inform decisions, and fairness concerns arise in both the prediction and decision stages.

By Yu Wang (Xinying), Violet (Xinying), Chen

safety

arXiv Machine Learning

Jun 11

Phi-Actor-Critic: Steering General-Sum Games to Pareto-Efficient Correlated Equilibria

arXiv:2606. 11284v1 Announce Type: cross Abstract: Real-world multi-agent systems, from traffic coordination to resource allocation, are often modeled as general-sum games where individual incentives conflict with collective welfare.

By Wongyu Lee, Francesco Lelli, Omran Ayoub, Massimo Tornatore

agents reinforcement-learning safety

arXiv Machine Learning

Aug 4

Inference-Time Policy Alignment for Fair Reinforcement Learning

arXiv:2608. 00175v1 Announce Type: new Abstract: Deep reinforcement learning (RL) agents achieve strong performance by optimizing scalar reward functions.

By Umer Siddique, Peilang Li, Conor Wallace, Yongcan Cao

llms agents reinforcement-learning safety

arXiv Machine Learning

Aug 4

Near-Optimal Reinforcement Learning for Constrained Recurrence Objectives

arXiv:2511. 19849v2 Announce Type: replace-cross Abstract: Recurrence objectives, where a target region must be visited infinitely often, are a fundamental class of specifications for Markov decision processes (MDPs) and form the core of $\omega$-regular and linear temporal logic (LTL) objectives.

By Dominik Wagner, Leon Witzman, Luke Ong

reinforcement-learning safety

arXiv Machine Learning

Jun 4

Optimal Transport under Group Fairness Constraints

arXiv:2601. 07144v3 Announce Type: replace-cross Abstract: Ensuring fairness in matching algorithms is a key challenge in allocating scarce resources and positions.

By Linus Bleistein, Mathieu Dagr\'eou, Francisco Andrade, Thomas Boudou, Aur\'elien Bellet

safety

arXiv AI

Jun 18

A Distributionally Robust Reinforcement Learning Framework for Constrained Urban EV Dispatch

arXiv:2604. 25848v2 Announce Type: replace Abstract: We study city-scale control of electric-vehicle (EV) ride-hailing fleets where dispatch, repositioning, and charging decisions must respect charger and feeder limits under uncertain, spatially correlated demand and travel times.

By An Nguyen, Hoang Nguyen, Phuong Le, Hung Pham, Cuong Do, Laurent El Ghaoui

agents reinforcement-learning

arXiv AI

Jun 10

Model-Based Reinforcement Learning in Discrete-Action Non-Markovian Reward Decision Processes

arXiv:2512. 14617v2 Announce Type: replace-cross Abstract: Many practical decision-making problems involve tasks whose success depends on the entire system history, rather than on achieving a state with desired properties.

By Alessandro Trapasso, Luca Iocchi, Fabio Patrizi

agents reinforcement-learning benchmarks

arXiv Machine Learning

Sep 21

Learning to Move Cities: Deep Meta-Models and Reinforcement Policies for Calibration and Control in Urban Networks

arXiv:2609.21945v1 Announce Type: new Abstract: Urban transportation networks present complex optimization challenges spanning calibration of high-fidelity simulators and real-time operational contro...

By Adewumi Augustine Adepitan, Christopher J. Haruna, Oluwasegun Adegoke, Ayooluwatomiwa Ajiboye, Oluwatobi Oluwasakin

agents reinforcement-learning benchmarks

arXiv AI

Jun 26

Deterministic Pareto-Optimal Policy Synthesis for Multi-Objective Reinforcement Learning

arXiv:2606. 26397v1 Announce Type: cross Abstract: Real-world decision-making often requires balancing multiple conflicting objectives, a challenge that standard Reinforcement Learning (RL) frequently addresses by aggregating rewards into a single scalar signal.

By Aniruddha Joshi, Niklas Lauffer, Sanjit Seshia

agents reinforcement-learning

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