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 …
On the opportunities and risks of foundation models
AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods for robotics and control
Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …
challenging control problems in robotics, but this performance comes at the cost of reduced …
A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution
AH Ganesh, B Xu - Renewable and Sustainable Energy Reviews, 2022 - Elsevier
The impact of internal combustion engine-powered automobiles on climate change due to
emissions and the depletion of fossil fuels has contributed to the progress of electrified …
emissions and the depletion of fossil fuels has contributed to the progress of electrified …
[图书][B] Control systems and reinforcement learning
S Meyn - 2022 - books.google.com
A high school student can create deep Q-learning code to control her robot, without any
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …
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 …
Responsive safety in reinforcement learning by pid lagrangian methods
Lagrangian methods are widely used algorithms for constrained optimization problems, but
their learning dynamics exhibit oscillations and overshoot which, when applied to safe …
their learning dynamics exhibit oscillations and overshoot which, when applied to safe …
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 …
Recovery rl: Safe reinforcement learning with learned recovery zones
Safety remains a central obstacle preventing widespread use of RL in the real world:
learning new tasks in uncertain environments requires extensive exploration, but safety …
learning new tasks in uncertain environments requires extensive exploration, but safety …