arXiv AI June 2, 2026 By Hallah Shahid Butt, Qiong Huang, G\"okhan Demirel, Kevin F\"orderer, Erfan Tajalli-Ardekani, Simnon Waczowicz, Luigi Spatafora, Veit Hagenmeyer, Benjamin Sch\"afer

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

Read the original on arXiv AI →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 15

Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control

arXiv:2607. 12856v1 Announce Type: new Abstract: Buildings are expected to shift cooling loads in response to grid conditions.

By Takumi Shioda, Kohei Terashima, Tatsuo Nagai

llms reinforcement-learning fine-tuning 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 Machine Learning

Jun 30

Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning

arXiv:2606. 30316v1 Announce Type: new Abstract: This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers.

By Jan Stenner, Alexander Kilian, Sebastian Peitz, Hermann de Meer

reinforcement-learning benchmarks

Hugging Face Trending Papers

Jun 29

Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning

This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers. We introduce a reproducible fixed-day simulation framework with synthetic wind and price signals and delayed completion feedback, designed to be extensible toward more complex scenarios.

reinforcement-learning benchmarks

arXiv AI

Jul 21

Building2Building: A Large Scale Benchmark for Generalizable Real-World Reinforcement Learning

arXiv:2607. 16534v1 Announce Type: cross Abstract: Reinforcement learning (RL) has achieved strong results in control, yet learned policies remain brittle to changes in dynamics, action spaces, observation spaces, or goals, a critical limitation for real-world deployment.

By Vincent Taboga, Justin Veilleux, Doseok Jang, Anushree Rankawat, Pierre-Luc Bacon

reinforcement-learning benchmarks

arXiv AI

Jun 2

Interpretable Policy Distillation for Power Grid Topology Control

arXiv:2606. 00561v1 Announce Type: cross Abstract: Deep reinforcement learning (RL) offers a promising route to real-time power grid operation, yet large neural policies are costly to evaluate, hard to deploy on constrained hardware, and opaque to operators.

By Aleksandra Dmitruka, Karlis Freivalds

agents reinforcement-learning efficiency

Related stories

Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control

Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning

Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning

Building2Building: A Large Scale Benchmark for Generalizable Real-World Reinforcement Learning

Interpretable Policy Distillation for Power Grid Topology Control