Reinforcement Learning

We are concerned with the question of how an agent can acquire its own representations from sensory data. We restrict our focus to …

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

A talk on autonomously learning object-centric abstractions for high-level planning, given at a CoE-MaSS (Centre of Excellence in Mathematical and Statistical Sciences) seminar.

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 …

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

We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that …

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 …