This paper presents a fully risk-aware model predictive control (MPC) framework for chance-
constrained discrete-time linear control systems with process noise. Conditional value-at …

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 …

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 …

In the era of data-driven decision-making, real-world applications often face uncertainties
arising from noise, environmental shifts, and adversarial perturbations. These challenges …