We review simulation optimization methods and discuss how these methods underpin
modern artificial intelligence (AI) techniques. In particular, we focus on three areas …

Large-scale simulation optimization (SO) problems encompass both large-scale ranking-
and-selection problems and high-dimensional discrete or continuous SO problems …

Classical reinforcement learning (RL) aims to optimize the expected cumulative rewards. In
this work, we consider the RL setting where the goal is to optimize the quantile of the …

Classical reinforcement learning (RL) aims to optimize the expected cumulative reward. In
this work, we consider the RL setting where the goal is to optimize the quantile of the …

Conditional value-at-risk (CVaR) is a well-established tool for measuring risk. In this article,
we consider solving CVaR optimization problems within a general simulation context. We …

We propose policy gradient algorithms for solving a risk-sensitive reinforcement learning
(RL) problem in on-policy as well as off-policy settings. We consider episodic Markov …

Safe reinforcement learning (RL) is a popular and versatile paradigm to learn reward-
maximizing policies with safety guarantees. Previous works tend to express the safety …

We propose a new unbiased stochastic gradient estimator for a family of stochastic models
driven by uniform random numbers as inputs. Dropping the requirement that the tails of the …

Mainstream reinforcement learning (RL) typically focuses on maximizing expected
cumulative rewards. In this paper, we explore a risk-sensitive RL setting where the objective …

This article addresses the challenge of parameter calibration in stochastic models where the
likelihood function is not analytically available. We propose a gradient-based simulated …