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cs.AI2026
Per-Domain Generalizing Policies: On Learning Efficient and Robust Q-Value Functions (Extended Version with Technical Appendix)
Nicola J. Müller, Moritz Oster, Isabel Valera +2
Learning per-domain generalizing policies is a key challenge in learning for planning. Standard approaches learn state-value functions represented as graph neural networks using su…
cs.AI2025
Improved Generalized Planning with LLMs through Strategy Refinement and Reflection
Katharina Stein, Nils Hodel, Daniel Fišer +3
LLMs have recently been used to generate Python programs representing generalized plans in PDDL planning, i.e., plans that generalize across the tasks of a given PDDL domain. Previ…