Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Accurate automated medical image recognition, including classification and segmentation,
is one of the most challenging tasks in medical image analysis. Recently, deep learning …

The ability to discover approximately optimal policies in domains with sparse rewards is
crucial to applying reinforcement learning (RL) in many real-world scenarios. Approaches …

Aiming at a cleaner future power system, many regimes in the world have proposed their
ambitious decarbonizing plan, with increasing penetration of renewable energy sources …

The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key
role in determining its sample efficiency. Thus, improving over random exploration is crucial …

When searching for objects in cluttered environments, it is often necessary to perform
complex interactions in order to move occluding objects out of the way and fully reveal the …

Reinforcement Learning (RL) agents require the specification of a reward signal for learning
behaviours. However, introduction of corrupt or stochastic rewards can yield high variance in …

We propose a framework based on distributional reinforcement learning and recent attempts
to combine Bayesian parameter updates with deep reinforcement learning. We show that …

Continuous control tasks in reinforcement learning are important because they provide an
important framework for learning in high-dimensional state spaces with deceptive rewards …

The field of reinforcement learning offers a large variety of concepts and methods to tackle
sequential decision-making problems. This variety has become so large that choosing an …