As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …

The aim of this work was to develop and evaluate the reinforcement learning algorithm
VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for …

Many dynamic processes, including common scenarios in robotic control and reinforcement
learning (RL), involve a set of interacting subprocesses. Though the subprocesses are not …

The problem of\emph {Offline Policy Evaluation}(OPE) in Reinforcement Learning (RL) is a
critical step towards applying RL in real life applications. Existing work on OPE mostly focus …

Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of
hypothetical policies leveraging only offline log data. It is particularly useful in applications …

Many medical decision-making tasks can be framed as partially observed Markov decision
processes (POMDPs). However, prevailing two-stage approaches that first learn a POMDP …

In reinforcement learning, importance sampling is a widely used method for evaluating an
expectation under the distribution of data of one policy when the data has in fact been …

The paper provides the readers with the knowledge of how reinforcement learning (RL)
applications can be applied in medical diagnosis and healthcare. RL is a powerful and …

Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis
is highly challenging, because individual patients respond very differently to medical …

Introduction In recent years, reinforcement learning (RL) has gained traction in the
healthcare domain. In particular, RL methods have been explored for haemodynamic …