arXiv Machine Learning

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.

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 Machine Learning

Jul 31

Exact Action Values Are Not Enough: Rollout-Verified Reinforcement Fine-Tuning of a Reasoning Model for Multi-Zone VAV Control

arXiv:2607. 27914v1 Announce Type: new Abstract: Multi-zone variable-air-volume control must balance thermal comfort, indoor air quality, and electricity use across several continuous actuators.

By Takumi Shioda, Kohei Terashima, Tatsuo Nagai

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 20

Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC

arXiv:2510. 01475v2 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) has demonstrated significant performance improvements over today's control methods for residential Heating, Ventilation, and Air Conditioning (HVAC), but deploying MPC often requires substantial engineering effort.

By Ozan Baris Mulayim, Elias N. Pergantis, Levi D. Reyes Premer, Bingqing Chen, Guannan Qu, Kevin J. Kircher, Mario Berg\'es

reinforcement-learning

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

arXiv Machine Learning

Jun 2

Quantifying the Energy Floor: Direct Measurement and Replay Buffer Bias in SAC-Based HVAC Control on sbsim

arXiv:2606. 01665v1 Announce Type: new Abstract: We quantify the energy floor -- the minimum achievable cost given action space constraints -- for Soft Actor-Critic (SAC) HVAC control on the sbsim calibrated building simulator.

By Bo Li, Chen Zhang

benchmarks safety

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 14

Time-Lag-Aware Deep Reinforcement Learning for Flexible Job-Shop Scheduling in PPVC Module Factories

arXiv:2607. 11725v1 Announce Type: cross Abstract: Prefabricated prefinished volumetric construction moves most building work into module factories, whose production floor operates as a flexible job shop.

By Ziheng Zhang, Wei Zhang

rag 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

Aug 6

Strategic Evaluation of Planning Strategies for LLM Agents in Cyber-Physical Systems

arXiv:2608. 04265v1 Announce Type: cross Abstract: Evaluations of LLM planning agents largely ask whether a task succeeds or a declared plan is followed.

By J. de Curt\`o, I. de Zarz\`a

llms agents 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

arXiv AI

Jun 26

State Representation Matters in Deep Reinforcement Learning: Application to Energy Trading

arXiv:2606. 27032v1 Announce Type: cross Abstract: Energy trading decisions depend not only on current market prices, but also on expected future market conditions, and operational constraints.

By Jesper Klicks, Sander Vr\v{z}ina, Vincent Fran\c{c}ois-Lavet

agents reinforcement-learning

arXiv AI

Jun 18

Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times

arXiv:2606. 19199v1 Announce Type: cross Abstract: The recent growth of EV adoption poses challenges for power systems, including increased peak demand and potential grid instability.

By Giuseppe Gabriele, Fabio Pavirani, Seyed Soroush Karimi Madahi, Chris Develder

agents reinforcement-learning

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