This paper develops a novel gradient-based reinforcement learning algorithm for solving dynamic quantile models with uncertainty. Unlike traditional approaches that rely on expected utility maximization, we focus on agents who evaluate outcomes based on specific quantiles of the utility...

This paper studies a dynamic quantile model for intertemporal decisions under uncertainty, in which the decision maker maximizes the τ-quantile of the stream of future utilities, for τ ∈ (0,1). We present two sets of contributions. First, we generalize existing results in directions that are...

Over the last decades, dynamic pricing has become increasingly popular. To solve pricing problems, however, is particularly challenging if the customers' and competitors' behavior are both strategic and unknown. Reinforcement Learning (RL) methods are promising for solving such dynamic problems...

This note illustrates, by reconsidering the seminal optimal speed-of-transition model of Aghion, P., and O. J. Blanchard. (1994. "On the Speed of Transition in Central Europe." NBER Macroeconomics Annual 9: 283-319), that optimal transition paths, in general, exhibit nonlinearities and...

In this study, we use the dynamic programming method introduced by Mirică (2004) to solve the well-known war game of attrition and attack as formulated by Isaacs (1965). By using this modern approach, we extend the classical framework to explore optimal strategies within the differential game...

We use dynamic programming, finite elements, and parallel computing to design and evaluate two-dimensional financial derivatives. Our dynamic program is flexible, as it divides the evaluation process into two components: one related to the dynamics of the underlying process and the other to the...