OMNI: Open-endedness via Models of human Notions of Interestingness

arXiv:2306.01711v3 Announce Type: replace-cross
Abstract: Open-ended algorithms aim to learn new, interesting behaviors forever. That requires a vast environment search space, but there are thus infinitely many possible tasks. Even after filtering for tasks the current agent can learn (i.e., learning progress), countless learnable yet uninteresting tasks remain (e.g., minor variations of previously learned tasks). An Achilles Heel of open-endedness research is the inability to quantify (and thus prioritize) tasks that are not just learnable, but also $\textit{interesting}$ (e.g., worthwhile …

arxiv cs.ai cs.lg human open-endedness type via