In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Communication networks are the key enabling technology for our digital society. In order to
sustain their critical services in the future, communication networks need to flexibly …

Deep reinforcement learning (RL) algorithms can use high-capacity deep networks to learn
directly from image observations. However, these high-dimensional observation spaces …

The reinforcement learning (RL) research area is very active, with an important number of
new contributions; especially considering the emergent field of deep RL (DRL). However a …

Deep state-space models (DSSMs) enable temporal predictions by learning the underlying
dynamics of observed sequence data. They are often trained by maximising the evidence …

To learn directed behaviors in complex environments, intelligent agents need to optimize
objective functions. Various objectives are known for designing artificial agents, including …

Flow is a subjective state characterized by immersion and engagement in one's current
activity. The benefits of flow for productivity and health are well-documented, but a rigorous …

The objective of lifelong reinforcement learning (RL) is to optimize agents which can
continuously adapt and interact in changing environments. However, current RL approaches …

Learning about many things can provide numerous benefits to a reinforcement learning
system. For example, learning many auxiliary value functions, in addition to optimizing the …

Abstract System identification of complex and nonlinear systems is a central problem for
model predictive control and model-based reinforcement learning. Despite their complexity …