The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history.
In more recent times, however, communication and collaboration between the two fields has …

The emergence of powerful artificial intelligence (AI) is defining new research directions in
neuroscience. To date, this research has focused largely on deep neural networks trained …

Many domains of science have developed complex simulations to describe phenomena of
interest. While these simulations provide high-fidelity models, they are poorly suited for …

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 …

Abstract We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep
reinforcement learning combining model-free and model-based aspects. In contrast to most …

We present OP3, a framework for model-based reinforcement learning that acquires object
representations from raw visual observations without supervision and uses them to predict …

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

Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve
performance, but often suffers from task interference which reduces the benefits of transfer …

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