Paper-Conference

While reinforcement learning has achieved remarkable successes in several domains, its real-world application is limited due to many …

We address the problem of efficiently learning high-level abstractions for task-level robot planning. Existing approaches require large …

Learning disentangled representations with variational autoencoders (VAEs) is often attributed to the regularisation component of the …

We tackle the problem of open-ended learning by introducing a method that simultaneously evolves agents while also evolving …

In this work, we consider the problem of procedural content generation for video game levels. Prior approaches have relied on …

Procedurally generated video game content has the potential to drastically reduce the content creation budget of game developers and …

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 …

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

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