:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Liu, Kailiang, Chen, Ying, Borndörfer, Ralf, Koch, Thorsten
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2512.04918
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning
by: Chen, Junfeng, et al.
Published: (2024)

Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance
by: Qin, Zhaoming, et al.
Published: (2021)

Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty
by: Liu, Rui, et al.
Published: (2026)

Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
by: Zhang, Ce, et al.
Published: (2023)

Learning to Solve Job Shop Scheduling under Uncertainty
by: Infantes, Guillaume, et al.
Published: (2024)

Learning to Orchestrate Agents under Uncertainty
by: Oliver, Mary Chriselda Antony, et al.
Published: (2026)

Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfer
by: Alagha, Ahmed, et al.
Published: (2025)

Fairness in Ranking under Disparate Uncertainty
by: Rastogi, Richa, et al.
Published: (2023)

Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics
by: Hazra, Somnath, et al.
Published: (2025)

Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertainties
by: Paul, Steve, et al.
Published: (2024)

Optimizing Electric Bus Charging Scheduling with Uncertainties Using Hierarchical Deep Reinforcement Learning
by: Qi, Jiaju, et al.
Published: (2025)

Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty
by: Shi, Laixi, et al.
Published: (2024)

A Comparison of Models for Rolling Stock Scheduling
by: Grimm, Boris, et al.
Published: (2023)

Task Scheduling & Forgetting in Multi-Task Reinforcement Learning
by: Speckmann, Marc, et al.
Published: (2025)

Learning to Optimize Job Shop Scheduling Under Structural Uncertainty
by: Zhang, Rui, et al.
Published: (2026)

Multi-Agent Reinforcement Learning for Maritime Operational Technology Cyber Security
by: Wilson, Alec, et al.
Published: (2024)

DUE: A Deep Learning Framework and Library for Modeling Unknown Equations
by: Chen, Junfeng, et al.
Published: (2025)

RL-MSA: a Reinforcement Learning-based Multi-line bus Scheduling Approach
by: Liu, Yingzhuo
Published: (2024)

Dynamic Operating System Scheduling Using Double DQN: A Reinforcement Learning Approach to Task Optimization
by: Sun, Xiaoxuan, et al.
Published: (2025)

Constrained Optimization of Charged Particle Tracking with Multi-Agent Reinforcement Learning
by: Kortus, Tobias, et al.
Published: (2025)

Belief States for Cooperative Multi-Agent Reinforcement Learning under Partial Observability
by: Pritz, Paul J., et al.
Published: (2025)

Clustering scientific publications: lessons learned through experiments with a real citation network
by: Huong, Vu Thi, et al.
Published: (2025)

Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning
by: Malarkkan, Arun Vignesh, et al.
Published: (2026)

AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling
by: Hamzeh, Hamed
Published: (2026)

Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception
by: Liu, Yandi, et al.
Published: (2025)

Scalable Reinforcement Learning for Virtual Machine Scheduling
by: Sheng, Junjie, et al.
Published: (2025)

MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery
by: Li, Dong, et al.
Published: (2026)

Electric Bus Scheduling with Non-Linear Charging, Power Grid Bottlenecks, and Dynamic Recharge Rates
by: Löbel, Fabian, et al.
Published: (2024)

Multi-Agent Reinforcement Learning with Submodular Reward
by: Chen, Wenjing, et al.
Published: (2026)

Adaptive Double-Booking Strategy for Outpatient Scheduling Using Multi-Objective Reinforcement Learning
by: Amalina, Ninda Nurseha, et al.
Published: (2026)

Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learning
by: Wang, Tongxi, et al.
Published: (2026)

Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
by: Liu, Shunyu, et al.
Published: (2022)

Multi-Agent Reinforcement Learning-Based UAV Pathfinding for Obstacle Avoidance in Stochastic Environment
by: Wu, Qizhen, et al.
Published: (2023)

Fully Independent Communication in Multi-Agent Reinforcement Learning
by: Pina, Rafael, et al.
Published: (2024)

Learning to Steer Markovian Agents under Model Uncertainty
by: Huang, Jiawei, et al.
Published: (2024)

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games
by: Li, Zhengyang, et al.
Published: (2025)

Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
by: Li, Simin, et al.
Published: (2023)

Double Distillation Network for Multi-Agent Reinforcement Learning
by: Zhou, Yang, et al.
Published: (2025)

The Living Forecast: Evolving Day-Ahead Predictions into Intraday Reality
by: Bölat, Kutay, et al.
Published: (2025)

Hierarchical Reinforcement Learning for Swarm Confrontation with High Uncertainty
by: Wu, Qizhen, et al.
Published: (2024)