# Learning Domain-Independent Planning Heuristics with Hypergraph Networks

arXiv: https://arxiv.org/abs/1911.13101 · HTML: https://mlsanity.com/paper/1911.13101/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2019.

## Built on by (most cited first, 30 of 50)

- [Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis](https://mlsanity.com/paper/2312.08782/) (2023)
- [Can Graph Learning Improve Planning in LLM-based Agents?](https://mlsanity.com/paper/2405.19119/) (2024)
- [Learning Domain-Independent Heuristics for Grounded and Lifted Planning](https://mlsanity.com/paper/2312.11143/) (2023)
- [Classical Planning in Deep Latent Space](https://mlsanity.com/paper/2107.00110/) (2021)
- [Learning Generalized Policies Without Supervision Using GNNs](https://mlsanity.com/paper/2205.06002/) (2022)
- [Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators](https://mlsanity.com/paper/2109.14830/) (2021)
- [Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning](https://mlsanity.com/paper/2403.16508/) (2024)
- [Classical Planning with LLM-Generated Heuristics: Challenging the State of the Art with Python Code](https://mlsanity.com/paper/2503.18809/) (2025)
- [Learning Efficient Abstract Planning Models that Choose What to Predict](https://mlsanity.com/paper/2208.07737/) (2022)
- [Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width: Extended Version](https://mlsanity.com/paper/2203.14852/) (2022)
- [Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal](https://mlsanity.com/paper/2310.19463/) (2023)
- [A Research Agenda for AI Planning in the Field of Flexible Production Systems](https://mlsanity.com/paper/2112.15484/) (2021)
- [Language Models For Generalised PDDL Planning: Synthesising Sound and Programmatic Policies](https://mlsanity.com/paper/2508.18507/) (2025)
- [NeSIG: A Neuro-Symbolic Method for Learning to Generate Planning Problems](https://mlsanity.com/paper/2301.10280/) (2023)
- [On Using Admissible Bounds for Learning Forward Search Heuristics](https://mlsanity.com/paper/2308.11905/) (2023)
- [Graph Learning for Numeric Planning](https://mlsanity.com/paper/2410.24080/) (2024)
- [Hierarchical Decomposition and Analysis for Generalized Planning](https://mlsanity.com/paper/2212.02823/) (2022)
- [Scale-Adaptive Balancing of Exploration and Exploitation in Classical Planning](https://mlsanity.com/paper/2305.09840/) (2023)
- [Towards Learning Foundation Models for Heuristic Functions to Solve Pathfinding Problems](https://mlsanity.com/paper/2406.02598/) (2024)
- [Leveraging Action Relational Structures for Integrated Learning and Planning](https://mlsanity.com/paper/2504.20318/) (2025)
- [Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning](https://mlsanity.com/paper/2206.14298/) (2022)
- [Sampling from Pre-Images to Learn Heuristic Functions for Classical Planning](https://mlsanity.com/paper/2207.03336/) (2022)
- [Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planning](https://mlsanity.com/paper/2211.13316/) (2022)
- [Graph Neural Network Based Action Ranking for Planning](https://mlsanity.com/paper/2412.04752/) (2024)
- [Learning General Policies with Policy Gradient Methods](https://mlsanity.com/paper/2512.19366/) (2025)
- [A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks](https://mlsanity.com/paper/2102.04518/) (2021)
- [Meta-Reinforcement Learning for Heuristic Planning](https://mlsanity.com/paper/2107.02603/) (2021)
- [Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning](https://mlsanity.com/paper/2202.00907/) (2022)
- [Hierarchical Reinforcement Learning with AI Planning Models](https://mlsanity.com/paper/2203.00669/) (2022)
- [A Differentiable Loss Function for Learning Heuristics in A*](https://mlsanity.com/paper/2209.05206/) (2022)

## Abstract (arXiv)

We present the first approach capable of learning domain-independent planning heuristics entirely from scratch. The heuristics we learn map the hypergraph representation of the delete-relaxation of the planning problem at hand, to a cost estimate that approximates that of the least-cost path from the current state to the goal through the hypergraph. We generalise Graph Networks to obtain a new framework for learning over hypergraphs, which we specialise to learn planning heuristics by training over state/value pairs obtained from optimal cost plans. Our experiments show that the resulting architecture, STRIPS-HGNs, is capable of learning heuristics that are competitive with existing delete-relaxation heuristics including LM-cut. We show that the heuristics we learn are able to generalise across different problems and domains, including to domains that were not seen during training.