Abstract: In this talk I will cover our recent publication "Open-Ended Learning Leads to Generally Capable Agents". In this work we turn our attention to how to create embodied agents in simulation that can generalise to unseen test tasks and exhibit generally capable behaviour. I will introduce our XLand procedurally generated environment, and the open-ended learning algorithms that allow us to train agents to cover this vast environment space. This results in agents that are capable across a wide range of held-out test tasks including hide-and-seek and capture-the-flag, and we will explore these results and the emergent behaviours and representations of the agent.

Bio: Max is a research scientist at DeepMind, where he leads the Open-Ended Learning research team. Known in the past for awesome work on XLand and StarCraft. He co-founded Vision Factory which was acquired by Google in 2014, and completed his PhD at the Visual Geometry Group at the University of Oxford under the supervision of Prof. Andrew Zisserman and Prof. Andrea Vedaldi.