arXiv AI

Reinforcement Learning-Based Laser Cutting Machine Parameter Optimization

arXiv:2608. 10549v1 Announce Type: new Abstract: Achieving high accuracy in laser-based cutting of optical films requires careful tuning of parameters such as focal length and laser power beam, adjusted according to the specific properties of each film type.

arXiv AI

Jun 19

Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufacturing

arXiv:2606. 20087v1 Announce Type: new Abstract: Additive manufacturing process optimization requires precise parameter control to minimize defects such as porosity.

By Kianoush Aqabakee, Leonardo Stella

agents reinforcement-learning

arXiv Machine Learning

Jun 30

Reinforcement Learning with a Bilevel World-Model Architecture for Scan-Order Optimisation in Laser Directed Energy Deposition

arXiv:2605. 25063v2 Announce Type: replace Abstract: Scan-order design in laser directed energy deposition (LDED) is a delayed, path-dependent thermo-mechanical decision problem, because sequence quality becomes observable only after the complete deposition and cooling cycle.

By Xian Wu, Haoran Li, Yuanqi Chu, Dongbin Zhao, Bin Wang

reinforcement-learning safety

arXiv AI

Aug 17

Reinforcement Learning-Based Production Scheduling in an Industry-Based Coating Scenario Using the Digital Model Playground

arXiv:2608. 14122v1 Announce Type: new Abstract: Production scheduling in complex manufacturing environments is challenging when sequence-dependent setup times, stochastic disturbances, and due-date constraints must be addressed simultaneously.

By Arne Kr\"oger, Ralf Buscherm\"ohle, Wilhelm Hasselbring, Henrik Wilbers

agents reinforcement-learning benchmarks

arXiv AI

Jun 10

Event-Driven Reinforcement Learning Enables Long-Horizon Control in Semiconductor Fabrication

arXiv:2606. 10705v1 Announce Type: cross Abstract: Reinforcement learning promises to optimize sequential decisions in large-scale systems.

By Yavar Yeganeh, Mahsa Shekari, Nicla Frigerio, Daniele Pagano, Andrea Matta

agents reinforcement-learning efficiency

arXiv AI

Jun 17

When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning

arXiv:2605. 05172v2 Announce Type: replace-cross Abstract: Behavior Cloning (BC) has emerged as a highly effective paradigm for robot learning.

By Lakshita Dodeja, Ondrej Biza, Shivam Vats, Stephen Hart, Stefanie Tellex, Robin Walters, Karl Schmeckpeper, Thomas Weng

reinforcement-learning robotics benchmarks

arXiv AI

Jul 28

When Every Simulation Counts: Value-Based Reinforcement Learning for Accelerated Photonics Inverse Design

arXiv:2607. 23469v1 Announce Type: cross Abstract: Photonic-crystal surface-emitting lasers (PCSELs) can combine high-power operation with narrow-divergence surface emission, but optimizing coupled parameters requires costly full-wave simulations.

By Longying Wen, Feiyang Wu, Jinglin Yu, Chongxian Yuan, Renjie Li, Zhaoyu Zhang

reinforcement-learning

arXiv Machine Learning

2d ago

RL-ABC: Reinforcement Learning for Accelerator Beamline Control

arXiv:2604.19146v2 Announce Type: replace Abstract: Particle accelerator beamline optimization is a high-dimensional control problem traditionally requiring significant expert intervention. We presen...

By Anwar Ibrahim, Fedor Ratnikov, Maxim Kaledin, Alexey Petrenko, Denis Derkach

agents reinforcement-learning

arXiv Machine Learning

Jun 2

LithoGRPO: Fast Inverse Lithography via GRPO Reinforced Flow Matching

arXiv:2606. 00228v1 Announce Type: new Abstract: In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask.

By Yao Lai, Xuyuan Xiong, Zeyue Xue, Guojin Chen, Jing Wang, Xihui Liu, Rui Zhang, Robert Mullins, Bei Yu, Ping Luo

diffusion reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Sep 11

Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models

The paper introduces a data‑driven self‑learning control method for highly flexible, modular manufacturing systems. It uses a model‑based reinforcement learning framework that incorporates approximate inverse process models, separating actuation dynamics from state‑space dynamics so that training occurs only in task space. A lightweight feedforward architecture for these inverse models is integrated into standard RL policy networks and tested on a laboratory modular production testbed, showing improved performance and faster training, especially for off‑policy algorithms.

By Andreas Schwung, Steve Yuwono, Sofiene Lassoued, Dorothea Schwung

reinforcement-learning

arXiv AI

Jul 21

Automated Reinforcement Learning: An Overview

arXiv:2201. 05000v3 Announce Type: replace-cross Abstract: Reinforcement Learning and, recently, Deep Reinforcement Learning are popular methods for solving sequential decision-making problems modeled as Markov Decision Processes.

By Reza Refaei Afshar, Joaquin Vanschoren, Uzay Kaymak, Rui Zhang, Yaoxin Wu, Wen Song, Yingqian Zhang

llms reinforcement-learning

arXiv Statistics ML

Sep 15

Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning framework

arXiv:2609.13234v1 Announce Type: cross Abstract: Industrialized construction imposes stringent precision requirements on robotic assembly of modular components such as prefabricated window units. In...

By Zekai Jin, Huiguang Wang, Xiaoning Sun, Yi Shao

rag agents reinforcement-learning robotics multimodal

arXiv AI

4d ago

Reward-rate Policy Gradient for Efficient Machine Learning Engineering Agents

arXiv:2609.36393v1 Announce Type: cross Abstract: Traditional reinforcement learning (RL) techniques focus on maximizing expected cumulative reward, where each action assumes to take a constant unit...

By Muhang Tian, Sherry Yang

llms agents reinforcement-learning

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