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 …

Discovering new treatments and personalizing existing ones is one of the major goals of
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …

Motivated by the many real-world applications of reinforcement learning (RL) that require
safe-policy iterations, we consider the problem of off-policy evaluation (OPE)---the problem …

This book was written to summarize and describe the state of the art of statistical methods
developed to address questions of estimation and inference for dynamic treatment regimes …

From household appliances to applications in robotics, engineered systems involving
complex dynamics can only be as effective as the algorithms that control them. While …

The management of invasive mechanical ventilation, and the regulation of sedation and
analgesia during ventilation, constitutes a major part of the care of patients admitted to …

We study the problem of off-policy policy optimization in Markov decision processes, and
develop a novel off-policy policy gradient method. Prior off-policy policy gradient …

Reinforcement-learning (RL) algorithms can automatically learn optimal control strategies
for nonlinear, possibly stochastic systems. A promising approach for RL control is …

We develop reinforcement learning trials for discovering individualized treatment regimens
for life‐threatening diseases such as cancer. A temporal‐difference learning method called …

Many reinforcement learning (RL) applications have combinatorial action spaces, where
each action is a composition of sub-actions. A standard RL approach ignores this inherent …