We propose a novel framework to solve risk-sensitive reinforcement learning problems
where the agent optimizes time-consistent dynamic spectral risk measures. Based on the …

We study the temporal robustness of stochastic signals. This topic is of particular interest in
interleaving processes such as multi-agent systems where communication and individual …

We develop an approach for solving time‐consistent risk‐sensitive stochastic optimization
problems using model‐free reinforcement learning (RL). Specifically, we assume agents …

We present a framework to interpret signal temporal logic (STL) formulas over discrete-time
stochastic processes in terms of the induced risk. Each realization of a stochastic process …

The wide availability of data coupled with the computational advances in artificial
intelligence and machine learning promise to enable many future technologies such as …

We propose a simple empirical representation of expectations such that: For a number of
samples above a certain threshold, drawn from any probability distribution with finite fourth …

In this paper, we propose a novel method called transcendental equation solver (TES) for
solving transcendental equations. The TES comprises a generator defined by a neural …

This paper conveys attitude and rate estimation without rate sensors by performing a critical
comparison, validated by extensive simulations. The two dominant approaches to facilitate …

We present a class of least squares reinforcement learning algorithms for optimal
consumption under elasticity of intertemporal substitution and risk aversion preferences. The …

We present a new uncertainty principle for risk-aware statistical estimation, effectively
quantifying the inherent trade-off between mean squared error (mse) and risk, the latter …