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 …

Deep neural networks (NNs) are powerful black box predictors that have recently achieved
impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in …

Modern machine learning methods including deep learning have achieved great success in
predictive accuracy for supervised learning tasks, but may still fall short in giving useful …

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …

In many real-world settings, a team of agents must coordinate its behaviour while acting in a
decentralised fashion. At the same time, it is often possible to train the agents in a …

The deep reinforcement learning community has made several independent improvements
to the DQN algorithm. However, it is unclear which of these extensions are complementary …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …