The paper provides an overview of the theory and applications of risk-sensitive Markov
decision processes. The term'risk-sensitive'refers here to the use of the Optimized Certainty …

We present a reinforcement learning (RL) approach for robust optimization of risk-aware
performance criteria. To allow agents to express a wide variety of risk-reward profiles, we …

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 …

Reinforcement Learning (RL) has gained substantial attention across diverse application
domains and theoretical investigations. Existing literature on RL theory largely focuses on …

Risk-sensitive reinforcement learning (RL) has become a popular tool to control the risk of
uncertain outcomes and ensure reliable performance in various sequential decision-making …

This review paper provides an in-depth overview of the evolution and advancements in
Robust Markov Decision Processes (RMDPs), a field of paramount importance for its role in …

Reinforcement learning algorithms utilizing policy gradients (PG) to optimize Conditional
Value at Risk (CVaR) face significant challenges with sample inefficiency, hindering their …

We consider the problem of estimating the asymptotic variance of a function defined on a
Markov chain, an important step for statistical inference of the stationary mean. We design a …

We consider the challenge of mitigating the generation of negative or toxic content by the
Large Language Models (LLMs) in response to certain prompts. We propose integrating risk …