Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Reinforcement learning (RL) has recently impressed the world with stunning results in
various applications. While the potential of RL is now well established, many critical aspects …

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 …

Due to spurious correlations, machine learning systems often fail to generalize to
environments whose distributions differ from the ones used at training time. Prior work …

Adaptive dynamic programming (ADP) methods have demonstrated their efficiency.
However, many of the applications for which ADP offers great potential, are also safety …

We propose a computationally efficient approach to safe reinforcement learning (RL) for
frequency regulation in power systems with high levels of variable renewable energy …

Developing model predictive control (MPC) schemes can be challenging for systems where
an accurate model is not available, or too costly to develop. With the increasing availability …

This paper introduces a new formulation for stochastic optimal control and stochastic
dynamic optimization that ensures safety with respect to state and control constraints. The …

The massive integration of renewable-based distributed energy resources (DERs) inherently
increases the energy system's complexity, especially when it comes to defining its …

A deep reinforcement learning application is investigated to control the emissions of a
compression ignition diesel engine. The main purpose of this study is to reduce the engine …