Cover Image

A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hoss, Jonathan, Schelling, Felix, Klarmann, Noah
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bridging the Sim-to-Real Gap in Reinforcement Learning-Based Industrial Dispatching through Execution Semantics
by: Hoss, Jonathan, et al.
Published: (2026)

Scalable Production Scheduling: Linear Complexity via Unified Homogeneous Graphs
by: Hoss, Jonathan, et al.
Published: (2026)

An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources
by: Link, Moritz, et al.
Published: (2026)

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

The Pump Scheduling Problem: A Real-World Scenario for Reinforcement Learning
by: Donâncio, Henrique, et al.
Published: (2022)

Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints
by: Okuda, Tadahisa, et al.
Published: (2026)

Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach
by: Shoush, Mahmoud, et al.
Published: (2023)

schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments
by: de Puiseau, Constantin Waubert, et al.
Published: (2023)

Safe Reinforcement Learning for Real-World Engine Control
by: Bedei, Julian, et al.
Published: (2025)

Zero-Shot Reinforcement Learning Under Partial Observability
by: Jeen, Scott, et al.
Published: (2025)

Safe Offline Reinforcement Learning with Real-Time Budget Constraints
by: Lin, Qian, et al.
Published: (2023)

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

Automatic Constraint Policy Optimization based on Continuous Constraint Interpolation Framework for Offline Reinforcement Learning
by: Han, Xinchen, et al.
Published: (2026)

FutureWorld: A Live Reinforcement Learning Environment for Predictive Agents with Real-World Outcome Rewards
by: Han, Zhixin, et al.
Published: (2026)

Real-World Reinforcement Learning of Active Perception Behaviors
by: Hu, Edward S., et al.
Published: (2025)

Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success
by: Bredis, George, et al.
Published: (2025)

Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning
by: Salaorni, Davide, et al.
Published: (2025)

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

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

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

Policy Constraint by Only Support Constraint for Offline Reinforcement Learning
by: Gao, Yunkai, et al.
Published: (2025)

Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions
by: Golowich, Noah, et al.
Published: (2024)

The Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation
by: Golowich, Noah, et al.
Published: (2024)

Agentic Reinforcement Learning for Real-World Code Repair
by: Zhu, Siyu, et al.
Published: (2025)

Inverse Reinforcement Learning With Constraint Recovery
by: Das, Nirjhar, et al.
Published: (2023)

RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning
by: Lei, Kun, et al.
Published: (2025)

Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments
by: Rauba, Paulius, et al.
Published: (2024)

ReLA: Representation Learning and Aggregation for Job Scheduling with Reinforcement Learning
by: Kwan, Zhengyi, et al.
Published: (2026)

A Survey of Constraint Formulations in Safe Reinforcement Learning
by: Wachi, Akifumi, et al.
Published: (2024)

Hierarchical Deep Reinforcement Learning Framework for Multi-Year Asset Management Under Budget Constraints
by: Fard, Amir, et al.
Published: (2025)

Learning Local Constraints for Reinforcement-Learned Content Generators
by: Bhaumik, Debosmita, et al.
Published: (2026)

DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control
by: Malmir, Mohammadhossein, et al.
Published: (2023)

Reinforcement Learning with $ω$-Regular Objectives and Constraints
by: Wagner, Dominik, et al.
Published: (2025)

Conservative Distributional Reinforcement Learning with Safety Constraints
by: Zhang, Hengrui, et al.
Published: (2022)

Probabilistic Constraint for Safety-Critical Reinforcement Learning
by: Chen, Weiqin, et al.
Published: (2023)

From Learning to Mastery: Achieving Safe and Efficient Real-World Autonomous Driving with Human-In-The-Loop Reinforcement Learning
by: Zeqiao, Li, et al.
Published: (2025)

Real-World Offline Reinforcement Learning from Vision Language Model Feedback
by: Venkataraman, Sreyas, et al.
Published: (2024)

RLVR-World: Training World Models with Reinforcement Learning
by: Wu, Jialong, et al.
Published: (2025)

OpenEstimate: Evaluating LLMs on Reasoning Under Uncertainty with Real-World Data
by: Renda, Alana, et al.
Published: (2025)

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