admissible-heuristics

This paper proposes a framework that learns admissible cost partitions for planning heuristics by leveraging Lagrangian dual equivalence, using a deep architecture with axial self-attention to guarantee admissibility by construction. It claims to be the first machine-learned heuristic provably guaranteed to be admissible.