  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. We propose an iterative notion of improvement
between successive generations of agents, rather than seeking to maximise a
singular objective, allowing us to quantify progress despite tasks being
incomparable in terms of achievable rewards. We show that through constructing
an open-ended learning process, which dynamically changes the training task
distributions and training objectives such that the agent never stops learning,
we achieve consistent learning of new behaviours. The resulting agent is able
to score reward in every one of our humanly solvable evaluation levels, with
behaviour generalising to many held-out points in the universe of tasks.
Examples of this zero-shot generalisation include good performance on Hide and
Seek, Capture the Flag, and Tag. Through analysis and hand-authored probe tasks
we characterise the behaviour of our agent, and find interesting emergent
heuristic behaviours such as trial-and-error experimentation, simple tool use,
option switching, and cooperation. Finally, we demonstrate that the general
capabilities of this agent could unlock larger scale transfer of behaviour
through cheap finetuning.
