We study a bilevel economic system, which we refer to as a Markov exchange economy
(MEE), from the point of view of multi-agent reinforcement learning (MARL). An MEE …

We study offline reinforcement learning (RL) in partially observable Markov decision
processes. In particular, we aim to learn an optimal policy from a dataset collected by a …

Linear contextual bandits represent a fundamental class of models with numerous real-
world applications, and it is critical to develop algorithms that can effectively manage noise …

The prevalence and importance of algorithmic two-sided marketplaces has drawn attention
to the issue of fairness in such settings. Algorithmic decisions are used in assigning students …

In this paper, we study gap-dependent regret guarantees for risk-sensitive reinforcement
learning based on the entropic risk measure. We propose a novel definition of sub-optimality …

We study multi-agent reinforcement learning in the setting of episodic Markov decision
processes, where many agents cooperate via communication through a central server. We …

We initiate the study of statistical inference and A/B testing for first-price pacing equilibria
(FPPE). The FPPE model captures the dynamics resulting from large-scale first-price auction …

Two-sided online matching platforms have been employed in various markets. However,
agents' preferences in present market are usually implicit and unknown and must be learned …

The problem of matching markets has been studied for a long time in the literature due to its
wide range of applications. Finding a stable matching is a common equilibrium objective in …

We study a heterogeneous agent macroeconomic model with an infinite number of
households and firms competing in a labor market. Each household earns income and …