# Learning to Rank for Synthesizing Planning Heuristics

arXiv: https://arxiv.org/abs/1608.01302 · HTML: https://mlsanity.com/paper/1608.01302/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2016.

## Built on by (most cited first, 22 of 22)

- [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)
- [Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal](https://mlsanity.com/paper/2310.19463/) (2023)
- [Representation, learning, and planning algorithms for geometric task and motion planning](https://mlsanity.com/paper/2203.04605/) (2022)
- [Learning Graph Search Heuristics](https://mlsanity.com/paper/2212.03978/) (2022)
- [Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery](https://mlsanity.com/paper/2203.04120/) (2022)
- [Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning](https://mlsanity.com/paper/2404.05235/) (2024)
- [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)
- [Scale-Adaptive Balancing of Exploration and Exploitation in Classical Planning](https://mlsanity.com/paper/2305.09840/) (2023)
- [Leveraging Action Relational Structures for Integrated Learning and Planning](https://mlsanity.com/paper/2504.20318/) (2025)
- [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)
- [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)
- [Learning Search-Space Specific Heuristics Using Neural Networks](https://mlsanity.com/paper/2306.04019/) (2023)
- [Deep Learning for Generalised Planning with Background Knowledge](https://mlsanity.com/paper/2410.07923/) (2024)
- [Weisfeiler-Leman Features for Planning: A 1,000,000 Sample Size Hyperparameter Study](https://mlsanity.com/paper/2508.18515/) (2025)
- [Learning Admissible Heuristics for A*: Theory and Practice](https://mlsanity.com/paper/2509.22626/) (2025)
- [Graph Learning for Planning: The Story Thus Far and Open Challenges](https://mlsanity.com/paper/2412.02136/) (2024)
- [Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning](https://mlsanity.com/paper/2505.13372/) (2025)
- [Learning Admissible Heuristics via Cost Partitioning](https://mlsanity.com/paper/2606.04597/) (2026)

## Abstract (arXiv)

We investigate learning heuristics for domain-specific planning. Prior work framed learning a heuristic as an ordinary regression problem. However, in a greedy best-first search, the ordering of states induced by a heuristic is more indicative of the resulting planner's performance than mean squared error. Thus, we instead frame learning a heuristic as a learning to rank problem which we solve using a RankSVM formulation. Additionally, we introduce new methods for computing features that capture temporal interactions in an approximate plan. Our experiments on recent International Planning Competition problems show that the RankSVM learned heuristics outperform both the original heuristics and heuristics learned through ordinary regression.