Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Non-terrestrial networks (NTNs) traditionally have certain limited applications. However, the
recent technological advancements and manufacturing cost reduction opened up myriad …

Batch reinforcement learning (RL) is important to apply RL algorithms to many high stakes
tasks. Doing batch RL in a way that yields a reliable new policy in large domains is …

Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the
environment. We aim to tackle a more challenging problem: learning a safe policy from an …

Reinforcement Learning (RL) algorithms have had tremendous success in simulated
domains. These algorithms, however, often cannot be directly applied to physical systems …

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …

We study the\emph {offline reinforcement learning}(offline RL) problem, where the goal is to
learn a reward-maximizing policy in an unknown\emph {Markov Decision Process}(MDP) …

We provide theoretical investigations into off-policy evaluation in reinforcement learning
using function approximators for (marginalized) importance weights and value functions. Our …

Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision
policies without needing to conduct exploration, which is often costly or otherwise infeasible …

Reinforcement learning (RL) can be used to learn treatment policies and aid decision
making in healthcare. However, given the need for generalization over complex state/action …