Sample efficiency and risk-awareness are central to the development of practical
reinforcement learning (RL) for complex decision-making. The former can be addressed by …

Designing hierarchical reinforcement learning algorithms that exhibit safe behaviour is not
only vital for practical applications but also facilitates a better understanding of an agent's …

Abstract General Value Function (GVF) is a powerful tool to represent both the {\em
predictive} and {\em retrospective} knowledge in reinforcement learning (RL). In practice …

Robust Reinforcement Learning tries to make predictions more robust to changes in the
dynamics or rewards of the system. This problem is particularly important when the …

Transfer learning in reinforcement learning (RL) has become a pivotal strategy for improving
data efficiency in new, unseen tasks by utilizing knowledge from previously learned tasks …

Many reinforcement learning approaches rely on temporal-difference (TD) learning to learn
a critic. However, TD-learning updates can be high variance. Here, we introduce a model …

General Value Functions (GVFs)(Sutton et al, 2011) are an established way to represent
predictive knowledge in reinforcement learning. Each GVF computes the expected return for …

General Value Function (GVF) is a powerful tool to represent both the {\em predictive} and
{\em retrospective} knowledge in reinforcement learning (RL). In practice, often multiple …

Human-machine collaboration is a promising training framework aimed at learning optimal
strategies in high-cost exploration scenarios. However, such work is challenging. On one …

Optimal experimental design (OED) is a statistical approach aimed at designing experiments
in order to extract maximum information from them. It entails carefully selecting experimental …