Abstract Off-Policy Evaluation (OPE) serves as one of the cornerstones in Reinforcement
Learning (RL). Fitted Q Evaluation (FQE) with various function approximators, especially …

Offline reinforcement learning (RL), where the agent aims to learn the optimal policy based
on the data collected by a behavior policy, has attracted increasing attention in recent years …

Offline reinforcement learning, which aims at optimizing sequential decision-making
strategies with historical data, has been extensively applied in real-life applications. State-Of …

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 …

The offline reinforcement learning (RL) problem is often motivated by the need to learn data-
driven decision policies in financial, legal and healthcare applications. However, the learned …

We study semiparametrically efficient estimation in off-policy evaluation (OPE) where the
underlying Markov decision process (MDP) is linear with a known feature map. We …

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 …

This paper endeavors to augment the robustness of offline reinforcement learning (RL) in
scenarios laden with heavy-tailed rewards, a prevalent circumstance in real-world …

In offline reinforcement learning (RL), the absence of active exploration calls for attention on
the model robustness to tackle the sim-to-real gap, where the discrepancy between the …

Mirror descent value iteration (MDVI), an abstraction of Kullback-Leibler (KL) and entropy-
regularized reinforcement learning (RL), has served as the basis for recent high-performing …