In this paper, an event-triggered control scheme with periodic characteristic is developed for
nonlinear discrete-time systems under an actor–critic architecture of reinforcement learning …

Safety and stability are common requirements for robotic control systems; however,
designing safe, stable controllers remains difficult for nonlinear and uncertain models. We …

This paper investigates the deep reinforcement learning based secure control problem for
cyber–physical systems (CPS) under false data injection attacks. We describe the CPS …

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 …

The problem of learning-based control for robots has been extensively studied, whereas the
security issue under malicious adversaries has not been paid much attention to. Malicious …

In this paper, we introduce a new reinforcement learning (RL) based neural architecture
search (NAS) methodology for effective and efficient generative adversarial network (GAN) …

Reinforcement learning (RL) is promising for complicated stochastic nonlinear control
problems. Without using a mathematical model, an optimal controller can be learned from …

This paper presents a novel model-reference reinforcement learning algorithm for the
intelligent tracking control of uncertain autonomous surface vehicles with collision …

The lack of stability guarantee restricts the practical use of learning-based methods in core
control problems in robotics. We develop new methods for learning neural control policies …

Developing stable controllers for large-scale networked dynamical systems is crucial but has
long been challenging due to two key obstacles: certifiability and scalability. In this paper …