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 …

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 …

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 …

It has been proven that advanced building control, like model predictive control (MPC), can
notably reduce the energy use and mitigate greenhouse gas emissions. However, despite …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

We propose a robust data-driven model predictive control (MPC) scheme to control linear
time-invariant systems. The scheme uses an implicit model description based on behavioral …

Tight estimation of the Lipschitz constant for deep neural networks (DNNs) is useful in many
applications ranging from robustness certification of classifiers to stability analysis of closed …

This article surveys reinforcement learning from the perspective of optimization and control,
with a focus on continuous control applications. It reviews the general formulation …

The optimal performance of robotic systems is usually achieved near the limit of state and
input bounds. Model predictive control (MPC) is a prevalent strategy to handle these …

Reinforcement learning is a powerful paradigm for learning optimal policies from
experimental data. However, to find optimal policies, most reinforcement learning algorithms …