Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only
maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a …

Convex Reinforcement Learning (RL) is a recently introduced framework that generalizes
the standard RL objective to any convex (or concave) function of the state distribution …

Quantitative trading (QT), which refers to the usage of mathematical models and data-driven
techniques in analyzing the financial market, has been a popular topic in both academia and …

Restricting the variance of a policy's return is a popular choice in risk-averse Reinforcement
Learning (RL) due to its clear mathematical definition and easy interpretability. Traditional …

Abstract The classic Reinforcement Learning (RL) formulation concerns the maximization of
a scalar reward function. More recently, convex RL has been introduced to extend the RL …

Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

We present a mean-variance policy iteration (MVPI) framework for risk-averse control in a
discounted infinite horizon MDP optimizing the variance of a per-step reward random …

Abstract The Off-Policy Evaluation (OPE) problem consists in evaluating the performance of
new policies from the data collected by another one. OPE is crucial when evaluating a new …

This work considers the problem of a trader who must manage the Credit Valuation
Adjustment (CVA) of a derivative, defined as the risk-neutral expectation of losses incurred if …