Enforcing hard constraints with soft barriers: Safe reinforcement learning in unknown stochastic environments
It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an
unknown and stochastic environment under hard constraints that require the system state …
unknown and stochastic environment under hard constraints that require the system state …
How good are learning-based control vs model-based control for load shifting? Investigations on a single zone building energy system
Both model predictive control (MPC) and deep reinforcement learning control (DRL) have
been presented as a way to approximate the true optimality of a dynamic programming …
been presented as a way to approximate the true optimality of a dynamic programming …
Phyllis: Physics-Informed Lifelong Reinforcement Learning for Data Center Cooling Control
Deep reinforcement learning (DRL) has shown good performance in data center cooling
control for improving energy efficiency. The main challenge in deploying the DRL agent to …
control for improving energy efficiency. The main challenge in deploying the DRL agent to …
Physics-Informed Data Denoising for Real-Life Sensing Systems
Sensors measuring real-life physical processes are ubiquitous in today's interconnected
world. These sensors inherently bear noise that often adversely affects performance and …
world. These sensors inherently bear noise that often adversely affects performance and …
BEAR-Data: Analysis and Applications of an Open Multizone Building Dataset
We introduce BEAR-Data, an open dataset that captures the dynamics of a large multi-zone
building by providing measurements of zone temperature and corresponding HVAC …
building by providing measurements of zone temperature and corresponding HVAC …
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
Reinforcement learning (RL) is challenging in the common case of delays between events
and their sensory perceptions. State-of-the-art (SOTA) state augmentation techniques either …
and their sensory perceptions. State-of-the-art (SOTA) state augmentation techniques either …
Learning from Limited and Imperfect Data in Cyber-Physical System
S Xu - 2023 - search.proquest.com
Abstract Machine learning is seeping into every fabric in various practical domains such as
autonomous driving, wearable computing, and smart buildings. However, in the actual …
autonomous driving, wearable computing, and smart buildings. However, in the actual …