A review of safe reinforcement learning: Methods, theory and applications
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …
making tasks. However, safety concerns are raised during deploying RL in real-world …
Safe learning in robotics: From learning-based control to safe reinforcement learning
The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …
methods for real-world robotic deployments from both the control and reinforcement learning …
Review on model predictive control: An engineering perspective
Abstract Model-based predictive control (MPC) describes a set of advanced control
methods, which make use of a process model to predict the future behavior of the controlled …
methods, which make use of a process model to predict the future behavior of the controlled …
Learning-based model predictive control: Toward safe learning in control
Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …
sensing and computational capabilities in modern control systems, have led to a growing …
All you need to know about model predictive control for buildings
It has been proven that advanced building control, like model predictive control (MPC), can
notably reduce the energy use and mitigate greenhouse gas emissions. However, despite …
notably reduce the energy use and mitigate greenhouse gas emissions. However, despite …
Reinforcement learning for selective key applications in power systems: Recent advances and future challenges
With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …
modern power systems are confronted with new operational challenges, such as growing …
End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks
Reinforcement Learning (RL) algorithms have found limited success beyond simulated
applications, and one main reason is the absence of safety guarantees during the learning …
applications, and one main reason is the absence of safety guarantees during the learning …
A survey on data center cooling systems: Technology, power consumption modeling and control strategy optimization
Q Zhang, Z Meng, X Hong, Y Zhan, J Liu, J Dong… - Journal of Systems …, 2021 - Elsevier
Data center is a fundamental infrastructure of computers and networking equipment to
collect, store, process, and distribute huge amounts of data for a variety of applications such …
collect, store, process, and distribute huge amounts of data for a variety of applications such …
[HTML][HTML] Reinforced model predictive control (RL-MPC) for building energy management
Buildings need advanced control for the efficient and climate-neutral use of their energy
systems. Model predictive control (MPC) and reinforcement learning (RL) arise as two …
systems. Model predictive control (MPC) and reinforcement learning (RL) arise as two …
Learning for safety-critical control with control barrier functions
Modern nonlinear control theory seeks to endow systems with properties of stability and
safety, and have been deployed successfully in multiple domains. Despite this success …
safety, and have been deployed successfully in multiple domains. Despite this success …