Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence
(AI) and represents a step toward building autonomous systems with a higher-level …

The scale of Internet-connected systems has increased considerably, and these systems are
being exposed to cyberattacks more than ever. The complexity and dynamics of …

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 …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

Understanding and interacting with everyday physical scenes requires rich knowledge
about the structure of the world, represented either implicitly in a value or policy function, or …

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step
towards building autonomous systems with a higher level understanding of the visual world …

We present foundations for using Model Predictive Control (MPC) as a differentiable policy
class for reinforcement learning. This provides one way of leveraging and combining the …

The popularity of deep reinforcement learning (DRL) applications in economics has
increased exponentially. DRL, through a wide range of capabilities from reinforcement …

We introduce an approach for deep reinforcement learning (RL) that improves upon the
efficiency, generalization capacity, and interpretability of conventional approaches through …

We introduce an approach for augmenting model-free deep reinforcement learning agents
with a mechanism for relational reasoning over structured representations, which improves …