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Learning to Rank for Synthesizing Planning Heuristics

arXiv 1608.01302 ↗First posted 3 Aug 2016cs.AIcited by 22

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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.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Aug 2016.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 4; 2024: 6; 2025: 4; 2026: 1.

22 papers.

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  1. 2021Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generatorscited by 21
  2. 2024Return to Tradition: Learning Reliable Heuristics with Classical Machine Learningcited by 19
  3. 2023Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goalcited by 10
  4. 2022Representation, learning, and planning algorithms for geometric task and motion planningcited by 8
  5. 2022Learning Graph Search Heuristicscited by 7
  6. 2024Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planningcited by 6
  7. 2022Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discoverycited by 5
  8. 2023On Using Admissible Bounds for Learning Forward Search Heuristicscited by 5
  9. 2024Graph Learning for Numeric Planningcited by 5
  10. 2023Scale-Adaptive Balancing of Exploration and Exploitation in Classical Planningcited by 4
  11. 2025Leveraging Action Relational Structures for Integrated Learning and Planningcited by 4
  12. 2022Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planningcited by 3
10 more
  1. 2024Graph Neural Network Based Action Ranking for Planningcited by 3
  2. 2021A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networkscited by 2
  3. 2021Meta-Reinforcement Learning for Heuristic Planningcited by 2
  4. 2023Learning Search-Space Specific Heuristics Using Neural Networkscited by 2
  5. 2024Deep Learning for Generalised Planning with Background Knowledgecited by 2
  6. 2025Weisfeiler-Leman Features for Planning: A 1,000,000 Sample Size Hyperparameter Studycited by 2
  7. 2025Learning Admissible Heuristics for A*: Theory and Practicecited by 2
  8. 2024Graph Learning for Planning: The Story Thus Far and Open Challengescited by 0
  9. 2025Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learningcited by 0
  10. 2026Learning Admissible Heuristics via Cost Partitioningcited by 0