  Open-ended and AI-generating algorithms aim to continuously generate and
solve increasingly complex tasks indefinitely, offering a promising path toward
more general intelligence. To accomplish this grand vision, learning must occur
within a vast array of potential tasks. Existing approaches to automatically
generating environments are constrained within manually predefined, often
narrow distributions of environment, limiting their ability to create any
learning environment. To address this limitation, we introduce a novel
framework, OMNI-EPIC, that augments previous work in Open-endedness via Models
of human Notions of Interestingness (OMNI) with Environments Programmed in Code
(EPIC). OMNI-EPIC leverages foundation models to autonomously generate code
specifying the next learnable (i.e., not too easy or difficult for the agent's
current skill set) and interesting (e.g., worthwhile and novel) tasks.
OMNI-EPIC generates both environments (e.g., an obstacle course) and reward
functions (e.g., progress through the obstacle course quickly without touching
red objects), enabling it, in principle, to create any simulatable learning
task. We showcase the explosive creativity of OMNI-EPIC, which continuously
innovates to suggest new, interesting learning challenges. We also highlight
how OMNI-EPIC can adapt to reinforcement learning agents' learning progress,
generating tasks that are of suitable difficulty. Overall, OMNI-EPIC can
endlessly create learnable and interesting environments, further propelling the
development of self-improving AI systems and AI-Generating Algorithms. Project
website with videos: https://dub.sh/omniepic
