Transfer Learning

An open problem in artificial intelligence is how to learn and represent knowledge that is sufficient for a general agent that needs to …

We propose a method for autonomously learning an object-centric representation of a continuous and high-dimensional environment that is …

We leverage logical composition in reinforcement learning to create a framework that enables an agent to autonomously determine whether …

Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning …

We leverage logical composition in reinforcement learning to create a framework that enables an agent to autonomously determine whether …

The ability to compose learned skills to solve new tasks is an important property for lifelong-learning agents. In this work we …

The ability to produce novel behaviours from existing skills is an important property of lifelong-learning agents. We build on recent …

We present a framework for autonomously learning a portable representation that describes a collection of low-level continuous …

We propose a method for autonomously learning an object-centric representation of a highdimensional environment that is suitable for …

We propose a framework for defining a Boolean algebra over the space of tasks. This allows us to formulate new tasks in terms of the …