Real-world autonomous systems are often controlled using conventional model-based
control methods. But if accurate models of a system are not available, these methods may be …

Designing a stabilizing controller for nonlinear systems is a challenging task, especially for
high-dimensional problems with unknown dynamics. Traditional reinforcement learning …

Control Lyapunov function is a central tool in stabilization. It generalizes an abstract energy
function—a Lyapunov function—to the case of controlled systems. It is a known fact that most …

Trained deep reinforcement learning (DRL) based controllers can effectively control
dynamic systems where classical controllers can be ineffective and difficult to tune …

The application of reinforcement learning in mobile robotics faces the challenges of real-
world physical environments, in contrast to playground setups like video games. In a mobile …

Stability certification and identification of a safe and stabilizing initial set are two important
concerns in ensuring operational safety, stability, and robustness of dynamical systems. With …

This paper investigates the navigation problem of autonomous vehicles based on
reinforcement learning (RL) with both stability and smoothness guarantees. By introducing a …

Actor-Critic (AC) architecture has the salient feature, for the plethora of Reinforcement
Learning schemes, that two intertwining neural networks (NN) collaborate to deploy a motor …

A common setting of reinforcement learning (RL) is a Markov decision process (MDP) in
which the environment is a stochastic discrete-time dynamical system. Whereas MDPs are …

Reinforcement learning has received significant interest in recent years, due primarily to the
successes of deep reinforcement learning at solving many challenging tasks such as …