Differentiable Learning of Lifted Action Schemas for Classical Planning 事件

Differentiable Learning of Lifted Action Schemas for Classical Planning arXiv:2605.13282v2 Announce Type: replace Abstract: Classical planners can effectively solve very large deterministic MDPs represented in STRIPS or PDDL where states are sets of atoms over objects and relations, and lifted action schemas add or delete these atoms. This compact representation yields strong search heuristics and provides an ideal setting for structural generalization, since lifted relations and action schemas