A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

Effectively leveraging large, previously collected datasets in reinforcement learn-ing (RL) is
a key challenge for large-scale real-world applications. Offline RL algorithms promise to …

Many practical applications of reinforcement learning constrain agents to learn from a fixed
batch of data which has already been gathered, without offering further possibility for data …

Sepsis is the third leading cause of death worldwide and the main cause of mortality in
hospitals,–, but the best treatment strategy remains uncertain. In particular, evidence …

We study the problem of off-policy value evaluation in reinforcement learning (RL), where
one aims to estimate the value of a new policy based on data collected by a different policy …

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 …

Actor-critic methods are widely used in offline reinforcement learningpractice, but are not so
well-understood theoretically. We propose a newoffline actor-critic algorithm that naturally …

This position paper reflects on the state-of-the-art in decision-making under uncertainty. A
classical assumption is that probabilities can sufficiently capture all uncertainty in a system …

We study the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where
the goal is to estimate the performance of a policy from the data generated by another policy …

Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple
data analysis and pattern recognition tools to complex systems that achieve superhuman …