Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning and has been recently applied to solve a number of challenging problems. In this …

Understanding when reinforcement learning algorithms can make successful off-policy
predictions—and when the may fail to do so–remains an open problem. Typically, model …

We study off-policy evaluation (OPE) for partially observable MDPs (POMDPs) with general
function approximation. Existing methods such as sequential importance sampling …

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

Reinforcement learning has been successful across several applications in which agents
have to learn to act in environments with sparse feedback. However, despite this empirical …

Off-policy evaluation often refers to two related tasks: estimating the expected return of a
policy and estimating its value function (or other functions of interest, such as density ratios) …

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 …

This work aims to study off-policy evaluation (OPE) under scenarios where two key
reinforcement learning (RL) assumptions–temporal stationarity and individual homogeneity …

Theoretical guarantees in reinforcement learning (RL) are known to suffer multiplicative
blow-up factors with respect to the misspecification error of function approximation. Yet, the …

We study off-policy evaluation (OPE) in partially observable environments with complex
observations, with the goal of developing estimators whose guarantee avoids exponential …