We present an historical overview about the connections between the analysis of risk and
the control of autonomous systems. We offer two main contributions. Our first contribution is …

We propose a new risk-constrained formulation of the classical Linear Quadratic (LQ)
stochastic control problem for general partially-observed systems. Our framework is …

Distributional reinforcement learning (DRL) enhances the understanding of the effects of the
randomness in the environment by letting agents learn the distribution of a random return …

We study a risk-averse optimal control problem for a finite-horizon Borel model, where a
cumulative cost is assessed via exponential utility. The setting permits non-linear dynamics …

This paper proposes a risk-aware control approach to enforce safety for discrete-time
nonlinear systems subject to stochastic uncertainties. We derive some useful results on the …

This paper presents a self‐triggered control approach with the view of risks for discrete‐time
linear stochastic systems. More specifically, under the assumption that the first two moments …

This article introduces the notions of stability, ultimate boundedness, and positive invariance
for stochastic systems in view of risk. More specifically, those notions are defined in terms of …

Considering non-stationary environments in online optimization enables decision-maker to
effectively adapt to changes and improve its performance over time. In such cases, it is …

Distributional reinforcement learning (DRL) enhances the understanding of the effects of the
randomness in the environment by letting agents learn the distribution of a random return …

Efforts by the reinforcement learning community to close the sim-to-real gap have resulted in
policy optimization objectives which are distinct from, though related to, existing objectives in …