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 deep reinforcement learning-based adaptive control for USV interception mission
This paper aims to develop a safe learning scheme of the USV interception mission. A safe
Lyapunov boundary deep deterministic policy gradient (SLDDPG) algorithm is presented for …
Lyapunov boundary deep deterministic policy gradient (SLDDPG) algorithm is presented for …
Safe reinforcement learning with stability guarantee for motion planning of autonomous vehicles
Reinforcement learning with safety constraints is promising for autonomous vehicles, of
which various failures may result in disastrous losses. In general, a safe policy is trained by …
which various failures may result in disastrous losses. In general, a safe policy is trained by …
Constrained update projection approach to safe policy optimization
Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only
maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a …
maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a …
Safe reinforcement learning for model-reference trajectory tracking of uncertain autonomous vehicles with model-based acceleration
Applying reinforcement learning (RL) algorithms to control systems design remains a
challenging task due to the potential unsafe exploration and the low sample efficiency. In …
challenging task due to the potential unsafe exploration and the low sample efficiency. In …
Barrier Lyapunov function-based safe reinforcement learning for autonomous vehicles with optimized backstepping
Guaranteed safety and performance under various circumstances remain technically critical
and practically challenging for the wide deployment of autonomous vehicles. Safety-critical …
and practically challenging for the wide deployment of autonomous vehicles. Safety-critical …
Adaptive safe reinforcement learning with full-state constraints and constrained adaptation for autonomous vehicles
High-performance learning-based control for the typical safety-critical autonomous vehicles
invariably requires that the full-state variables are constrained within the safety region even …
invariably requires that the full-state variables are constrained within the safety region even …
Online reinforcement learning with passivity-based stabilizing term for real time overhead crane control without knowledge of the system model
Due to the existing uncertainties such as the payload mass and unmodeled dynamics in the
overhead crane system, classical model-based control methods yielding fixed control gain …
overhead crane system, classical model-based control methods yielding fixed control gain …
Secure adaptive event-triggered control for cyber–physical power systems under denial-of-service attacks
Secure control for cyber–physical power systems (CPPSs) under cyber attacks is a
challenging issue. Existing event-triggered control schemes are generally difficult to mitigate …
challenging issue. Existing event-triggered control schemes are generally difficult to mitigate …
An adaptive gain based approach for event-triggered state estimation with unknown parameters and sensor nonlinearities over wireless sensor networks
The distributed state and parameter estimation problem is investigated in this paper for
discrete-time nonlinear systems subject to sensor nonlinearities and stochastic disturbances …
discrete-time nonlinear systems subject to sensor nonlinearities and stochastic disturbances …