Article-Journal

Reinforcement Learning (RL) research often models environments as Markov decision processes. Yet many real-world tasks are …

An important problem in reinforcement learning is designing agents that learn to solve tasks safely in an environment. A common …

One avenue for creating a generally intelligent agent is to equip it with the ability to combine its previously learned behaviours to …

An important property of long-lived agents is the ability to reuse existing knowledge to solve new tasks. An appealing approach towards …

Combining reinforcement learning with language grounding is challenging as the agent needs to explore the environment while …

Reinforcement learning (RL) has recently made several significant advances using video games as a testbed. While many of these games …

Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry and great potential to …

We present a framework for the automatic encoding and repair of high-level tasks. Given a set of skills a robot can perform, our …

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