PowerZooJax is a JAX-based benchmark suite designed for reinforcement learning in power system operation. It offers five constrained Markov decision process tasks covering generation, transmission, distribution, distributed energy resources, and data center microgrids. By implementing power flow, economic dispatch, market clearing, and device dynamics as JAX computation graphs, the entire training and evaluation loop runs on the GPU, yielding significant speedups over CPU-based simulations and enabling standardized evaluation of policy returns, safety violations, and out-of-distribution stress conditions.
By Zhanhua Pan, Xiao Liu, Zhilong Cao, Jianhong Wang, Dawei Qiu
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
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.
By Qinghe Gao, Artur M. Schweidtmann
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
OpenHail is an open-source Gymnasium environment designed for controlling electric ride‑hailing fleets. It offers a fixed‑size observation–action interface that handles request assignment, repositioning, and charging, while its event‑driven simulator models pickup deadlines, vehicle job queues, battery dynamics, and finite‑capacity charging facilities with FIFO queues. The environment supports various decision‑epoch mechanisms—event‑driven, periodic, hybrid, and policy‑requested—allowing flexible policy interactions within a unified operational model, and includes tools for evaluation, metrics, and baseline policies.
By Tommaso Schettini, Nicholas D. Kullman, Jorge E. Mendoza
arXiv:2409. 19716v2 Announce Type: replace-cross Abstract: Constrained Reinforcement Learning (RL) has emerged as a significant research area within RL, where integrating constraints with rewards is crucial for enhancing safety and performance across diverse control tasks.
By Baohe Zhang, Lilli Frison, Thomas Brox, Joschka B\"odecker
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
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
arXiv:2607. 03140v1 Announce Type: new Abstract: Industrial automation increasingly demands control strategies that balance operational performance with strict energy efficiency requirements.
By Georg Sch\"afer, Jakob Rehrl, Stefan Huber, Simon Hirlaender
The paper proposes a hybrid PID–Deep Reinforcement Learning (DRL) controller for industrial processes, addressing the limitations of traditional PID controllers in complex, non‑linear, multi‑input environments. Using the Industrial Benchmark (IB) to test DRL, the authors develop a multi‑objective reward function and employ a TD3 agent to discover optimal settings for the IB’s ‘Gain’ and ‘Shift’ parameters. These parameters are then fed into a tuned PID controller, yielding a system that combines the optimal performance and efficiency of DRL with the reliability of classical control.
By Zhengyang (Cissy), Gu, Joseph E. Hernandez, John Burtenshaw, Sean Scott, Thomas Cook, Chris Couch
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
arXiv:2510. 17709v2 Announce Type: replace-cross Abstract: Training Reinforcement Learning (RL) policies using simulation models before deployment in real-world environments is a common strategy when real-world interaction is expensive.
By Akhil S Anand, Shambhuraj Sawant, Paavo Parmas, Jasper Hoffmann, Dirk Reinhardt, Sebastien Gros