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

The aim of this work is to study risk measures generated by distortion functions in a dynamic
discrete time setup and to investigate the corresponding dynamic coherent acceptability …

The concepts of a conditional set, a conditional inclusion relation and a conditional
Cartesian product are introduced. The resulting conditional set theory is sufficiently rich in …

We propose the star-shaped acceptability indexes as generalizations of both the
approaches of Cherny and Madan (2009) and Rosazza Gianin and Sgarra (2013) in the …

In this paper we consider a discrete‐time risk sensitive portfolio optimization over a long time
horizon with proportional transaction costs. We show that within the log‐return iid framework …

In this work we give a comprehensive overview of the time consistency property of dynamic
risk and performance measures, focusing on a the discrete time setup. The two key …

We present an arbitrage free theoretical framework for modeling bid and ask prices of
dividend paying securities in a discrete time setup using the theory of dynamic acceptability …

In this paper, we provide a flexible framework allowing for a unified study of time consistency
of risk measures and performance measures (also known as acceptability indices). The …

In a reinforcement learning (RL) setting, the agent's optimal strategy heavily depends on her
risk preferences and the underlying model dynamics of the training environment. These two …