arXiv Machine Learning

Deep reinforcement learning for process design: Review and perspective

arXiv:2308. 07822v2 Announce Type: replace Abstract: The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches.

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 Machine Learning

Aug 10

The Challenges of Using Reinforcement Learning for Controlling Industrial Energy Systems

arXiv:2605. 31044v2 Announce Type: replace Abstract: Reinforcement learning has shown promising results for optimizing the control of industrial energy systems, yet most existing studies remain limited to the application in simulation environments.

By Tobias Lademann, Th\'eo Vincent, Jan Peters, Matthias Weigold

reinforcement-learning

arXiv AI

2d ago

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 6

Learning to replenish: A hybrid deep reinforcement learning for dynamic inventory management in the pharmaceutical supply chains

arXiv:2606. 06201v1 Announce Type: new Abstract: Pharmaceutical supply chains (PSCs) struggle with inventory management (IM) due to unpredictable demand patterns and variable lead times associated with restocking.

By Amandeep Kaur, Gyan Prakash

reinforcement-learning benchmarks

arXiv AI

Jun 2

Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings

arXiv:2606. 02049v1 Announce Type: new Abstract: The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces significant complexity in energy systems.

By Hallah Shahid Butt, Qiong Huang, G\"okhan Demirel, Kevin F\"orderer, Erfan Tajalli-Ardekani, Simnon Waczowicz, Luigi Spatafora, Veit Hagenmeyer, Benjamin Sch\"afer

agents reinforcement-learning

arXiv AI

Jul 8

Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms

arXiv:2607. 06489v1 Announce Type: new Abstract: The dairy industry in Ireland has a large potential for the integration of renewable energy and the reduction of carbon emissions.

By Marcos Eduardo Cruz Victorio, Karl Mason

agents reinforcement-learning

arXiv AI

Aug 10

Flowing Through States: Neural ODE Regularization for Reinforcement Learning

arXiv:2608. 06595v1 Announce Type: cross Abstract: Neural networks applied to sequential decision-making tasks typically rely on latent representations of environment states.

By Mohamed Ghanem, Bernd Finkbeiner

rag agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 25

Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

arXiv:2606. 24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncertainties and modelling complexity.

By Haoyuan Deng, Yihong Zhou, Thomas Morstyn, Yi Wang

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 15

Learning optimal policies from event logs through reinforcement learning: a comparison of deep and MDP-based approaches

arXiv:2303. 09209v2 Announce Type: replace Abstract: Prescriptive Process Monitoring is an emerging area within Process Mining that focuses on recommending actions to optimize business outcomes.

By Stefano Branchi, Andrei Buliga, Chiara Di Francescomarino, Chiara Ghidini, Riccardo Graziosi, Francesca Meneghello, Massimiliano Ronzani

reinforcement-learning benchmarks

arXiv AI

Jun 26

EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning

arXiv:2606. 26327v1 Announce Type: cross Abstract: In actor-critic reinforcement learning, network architectures are typically manually designed.

By Boyun Zhang, Chao Wang, Kai Wu

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 10

Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms

arXiv:2607. 07769v1 Announce Type: cross Abstract: Starting from the utilization of deep neural networks to approximate the state-action value function that led to winning one of the most challenging games, to algorithmic advancements that allowed solving problems without even explicitly stating the rules of the challenge at hand, reinforcement learning research has been the center of remarkable scientific progress for the past decade.

By Ezgi Korkmaz

reinforcement-learning

arXiv AI

Aug 5

Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision Tasks

arXiv:2608. 03502v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents.

By Christophe D. Hounwanou, John Emeka Eze, Ya\'e Ulrich Gaba

llms agents reinforcement-learning

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