A review of safe reinforcement learning: Methods, theory and applications
Reinforcement learning (RL) has achieved tremendous success in many complex decision
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …
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 …
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 …
Physics-informed machine learning: A survey on problems, methods and applications
Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …
vision, reinforcement learning, and many scientific and engineering domains. In many real …
The safety filter: A unified view of safety-critical control in autonomous systems
Recent years have seen significant progress in the realm of robot autonomy, accompanied
by the expanding reach of robotic technologies. However, the emergence of new …
by the expanding reach of robotic technologies. However, the emergence of new …
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 …
Data-enabled predictive control: In the shallows of the DeePC
We consider the problem of optimal trajectory tracking for unknown systems. A novel data-
enabled predictive control (DeePC) algorithm is presented that computes optimal and safe …
enabled predictive control (DeePC) algorithm is presented that computes optimal and safe …
Natural policy gradient primal-dual method for constrained markov decision processes
We study sequential decision-making problems in which each agent aims to maximize the
expected total reward while satisfying a constraint on the expected total utility. We employ …
expected total reward while satisfying a constraint on the expected total utility. We employ …
Disturbance observers and extended state observers for marine vehicles: A survey
The operation performance of marine vehicles (MVs) is significantly vulnerable to external
disturbances induced by wind, waves, and ocean currents in complex marine environments …
disturbances induced by wind, waves, and ocean currents in complex marine environments …