# Leveraging Action Relational Structures for Integrated Learning and Planning

arXiv: https://arxiv.org/abs/2504.20318 · HTML: https://mlsanity.com/paper/2504.20318/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks](https://mlsanity.com/paper/1401.3839/) (2014)
- [Learning Domain-Independent Planning Heuristics with Hypergraph Networks](https://mlsanity.com/paper/1911.13101/) (2019)
- [ASNets: Deep Learning for Generalised Planning](https://mlsanity.com/paper/1908.01362/) (2019)
- [Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits](https://mlsanity.com/paper/2109.10129/) (2021)
- [Action Schema Networks: Generalised Policies with Deep Learning](https://mlsanity.com/paper/1709.04271/) (2017)
- [Learning Domain-Independent Heuristics for Grounded and Lifted Planning](https://mlsanity.com/paper/2312.11143/) (2023)
- [Learning to Rank for Synthesizing Planning Heuristics](https://mlsanity.com/paper/1608.01302/) (2016)
- [Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning](https://mlsanity.com/paper/2007.06702/) (2020)
- [Learning Generalized Policies Without Supervision Using GNNs](https://mlsanity.com/paper/2205.06002/) (2022)
- [Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning](https://mlsanity.com/paper/2403.16508/) (2024)
- [Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal](https://mlsanity.com/paper/2310.19463/) (2023)
- [Solving L1-regularized SVMs and related linear programs: Revisiting the effectiveness of Column and Constraint Generation](https://mlsanity.com/paper/1901.01585/) (2019)

## Built on by (most cited first, 4 of 4)

- [Language Models For Generalised PDDL Planning: Synthesising Sound and Programmatic Policies](https://mlsanity.com/paper/2508.18507/) (2025)
- [Weisfeiler-Leman Features for Planning: A 1,000,000 Sample Size Hyperparameter Study](https://mlsanity.com/paper/2508.18515/) (2025)
- [Symmetry-Invariant Novelty Heuristics via Unsupervised Weisfeiler-Leman Features](https://mlsanity.com/paper/2508.18520/) (2025)
- [Learning Admissible Heuristics via Cost Partitioning](https://mlsanity.com/paper/2606.04597/) (2026)

## Abstract (arXiv)

Recent advances in planning have explored using learning methods to help planning. However, little attention has been given to adapting search algorithms to work better with learning systems. In this paper, we introduce partial-space search, a new search space for classical planning that leverages the relational structure of actions given by PDDL action schemas -- a structure overlooked by traditional planning approaches. Partial-space search provides a more granular view of the search space and allows earlier pruning of poor actions compared to state-space search. To guide partial-space search, we introduce action set heuristics that evaluate sets of actions in a state. We describe how to automatically convert existing heuristics into action set heuristics. We also train action set heuristics from scratch using large training datasets from partial-space search. Our new planner, LazyLifted, exploits our better integrated search and learning heuristics and outperforms the state-of-the-art ML-based heuristic on IPC 2023 learning track (LT) benchmarks. We also show the efficiency of LazyLifted on high-branching factor tasks and show that it surpasses LAMA in the combined IPC 2023 LT and high-branching factor benchmarks.