Open-Ended Learning Leads to Generally Capable Agents

Understand

In this work we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a massive, rich space of challenges.

• We define a universe of tasks within an environment domain and demonstrate the ability to train agents that are generally capable across this vast space and beyond.
• The environment is natively multi-agent, spanning the continuum of competitive, cooperative, and independent games, which are situated within procedurally generated physical 3D worlds.
• The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the learning progress of an agent is an open research problem.