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.
arXiv:2607. 12856v1 Announce Type: new Abstract: Buildings are expected to shift cooling loads in response to grid conditions.
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.
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.
arXiv:2607. 22609v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used for decision-making and reasoning tasks, yet their potential as controllers for physical systems remains largely unexplored.
The paper introduces a compute‑aware three‑action controller for evaluator‑guided reasoning, deciding whether to trust the current answer, fallback, or expand inference. It scores terminal actions by predicted success and uses a rollout‑based surrogate for expansion, explicitly pricing compute cost. Experiments on width expansion and revision tasks show the controller improves utility over fixed policies while reducing online compute by about 30% and 16%, respectively, and demonstrates that evaluator‑guided reasoning is best viewed as resource allocation under uncertainty.
arXiv:2608. 07809v1 Announce Type: new Abstract: A world model is only useful for physical AI if it changes what the agent does, and only safe if it declines to do so when it is wrong.
The paper proposes a method for selectively querying language‑model advice in reinforcement learning by predicting the value of potential responses and only querying when the expected benefit outweighs the cost. It introduces a certified, response‑contingent metareasoning framework that guarantees near‑optimal advice usage under certain assumptions, and demonstrates that a calibrated controller with Qwen2.5 advisors can improve task performance while drastically reducing the number of advice calls on the BabyAI benchmark.
arXiv:2607. 11959v1 Announce Type: new Abstract: Greenhouse reinforcement learning can test climate-control ideas at a speed and scale that is difficult to achieve with crop experiments alone.
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.
The paper introduces NOMAD‑RL, a reinforcement learning controller for HVAC systems that learns to adapt across diverse thermal zones via a universal thermostat interface. It employs an adaptive domain randomization scheme using physics‑informed normalizing flows to generate realistic, multimodal training data, enabling the recurrent policy to handle partial observability. Experiments show NOMAD‑RL outperforms constant‑setpoint PID and non‑randomized RL, and rivals well‑tuned model predictive control, especially in multi‑zone scenarios.
arXiv:2605. 20256v2 Announce Type: replace Abstract: Reinforcement learning has become a cornerstone for aligning and unlocking the reasoning capabilities of large-scale models.