Imagen de Portada

ReLA: Representation Learning and Aggregation for Job Scheduling with Reinforcement Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Kwan, Zhengyi, Zhang, Wei, Ng, Aik Beng, Wang, Zhengkui, See, Simon
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Nutrition Estimation for Dietary Management: A Transformer Approach with Depth Sensing
por: Kwan, Zhengyi, et al.
Publicado: (2024)

Feature-based Graph Attention Networks Improve Online Continual Learning
por: Sim, Adjovi, et al.
Publicado: (2025)

Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
por: van Remmerden, Jesse, et al.
Publicado: (2024)

SegReConcat: A Data Augmentation Method for Voice Anonymization Attack
por: Arefeen, Ridwan, et al.
Publicado: (2025)

Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
por: Zhang, Cong, et al.
Publicado: (2022)

On-Device LLMs for SMEs: Challenges and Opportunities
por: Yee, Jeremy Stephen Gabriel, et al.
Publicado: (2024)

Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
por: Zhang, Cong, et al.
Publicado: (2024)

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

HGT-Scheduler: Deep Reinforcement Learning for the Job Shop Scheduling Problem via Heterogeneous Graph Transformers
por: Soykan, Bulent
Publicado: (2026)

Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling
por: de Puiseau, Constantin Waubert, et al.
Publicado: (2024)

Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling
por: Wang, Jiaqi, et al.
Publicado: (2026)

Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods
por: Reijnen, Robbert, et al.
Publicado: (2023)

Enhancing Modality Representation and Alignment for Multimodal Cold-start Active Learning
por: Shen, Meng, et al.
Publicado: (2024)

Learning on the Job: Test-Time Curricula for Targeted Reinforcement Learning
por: Hübotter, Jonas, et al.
Publicado: (2025)

Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data
por: Jia, Zeyu, et al.
Publicado: (2024)

Fairness-Aware Job Scheduling for Multi-Job Federated Learning
por: Shi, Yuxin, et al.
Publicado: (2024)

Multi-Modal One-Shot Federated Ensemble Learning for Medical Data with Vision Large Language Model
por: Wang, Naibo, et al.
Publicado: (2025)

Variational Approach for Job Shop Scheduling
por: Oh, Seung Heon, et al.
Publicado: (2026)

Self-Evaluation for Job-Shop Scheduling
por: Echeverria, Imanol, et al.
Publicado: (2025)

CoLiDR: Concept Learning using Aggregated Disentangled Representations
por: Sinha, Sanchit, et al.
Publicado: (2024)

Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Data
por: van Remmerden, Jesse, et al.
Publicado: (2025)

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

Measurement Scheduling for ICU Patients with Offline Reinforcement Learning
por: Ji, Zongliang, et al.
Publicado: (2024)

DAST: A Dual-Stream Voice Anonymization Attacker with Staged Training
por: Arefeen, Ridwan, et al.
Publicado: (2026)

Debiasing Reward Models by Representation Learning with Guarantees
por: Ng, Ignavier, et al.
Publicado: (2025)

SplAgger: Split Aggregation for Meta-Reinforcement Learning
por: Beck, Jacob, et al.
Publicado: (2024)

The Terminal Representation in Reinforcement Learning
por: Esterhuysen, Amir, et al.
Publicado: (2026)

Representation-Driven Reinforcement Learning
por: Nabati, Ofir, et al.
Publicado: (2023)

Learning-enabled Flexible Job-shop Scheduling for Scalable Smart Manufacturing
por: Moon, Sihoon, et al.
Publicado: (2024)

Continual Learning of Nonlinear Independent Representations
por: Sun, Boyang, et al.
Publicado: (2024)

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

Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learning
por: Wang, Changhong, et al.
Publicado: (2024)

Approximate Subgraph Matching with Neural Graph Representations and Reinforcement Learning
por: Li, Kaiyang, et al.
Publicado: (2026)

Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework
por: Hu, Chengpeng, et al.
Publicado: (2026)

Hybrid Imitation-Learning Motion Planner for Urban Driving
por: Gariboldi, Cristian, et al.
Publicado: (2024)

Toward Robust Signed Graph Learning through Joint Input-Target Denoising
por: Wu, Junran, et al.
Publicado: (2025)

Stackelberg Coupling of Online Representation Learning and Reinforcement Learning
por: Martinez, Fernando, et al.
Publicado: (2025)

Optimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogistics
por: Schneevogt, Malte, et al.
Publicado: (2024)

Diffusion Spectral Representation for Reinforcement Learning
por: Shribak, Dmitry, et al.
Publicado: (2024)

Spectral Representation-based Reinforcement Learning
por: Gao, Chenxiao, et al.
Publicado: (2025)