# ASNets: Deep Learning for Generalised Planning

arXiv: https://arxiv.org/abs/1908.01362 · HTML: https://mlsanity.com/paper/1908.01362/

## Key facts

- Cited by 42 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 4 Aug 2019.

## Built on by (most cited first, 30 of 42)

- [Can Graph Learning Improve Planning in LLM-based Agents?](https://mlsanity.com/paper/2405.19119/) (2024)
- [Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits](https://mlsanity.com/paper/2109.10129/) (2021)
- [Learning Domain-Independent Heuristics for Grounded and Lifted Planning](https://mlsanity.com/paper/2312.11143/) (2023)
- [Learning Generalized Policies Without Supervision Using GNNs](https://mlsanity.com/paper/2205.06002/) (2022)
- [Scaling-up Generalized Planning as Heuristic Search with Landmarks](https://mlsanity.com/paper/2205.04850/) (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)
- [Hierarchical generative modelling for autonomous robots](https://mlsanity.com/paper/2308.07775/) (2023)
- [A Research Agenda for AI Planning in the Field of Flexible Production Systems](https://mlsanity.com/paper/2112.15484/) (2021)
- [Symmetries and Expressive Requirements for Learning General Policies](https://mlsanity.com/paper/2409.15892/) (2024)
- [Language Models For Generalised PDDL Planning: Synthesising Sound and Programmatic Policies](https://mlsanity.com/paper/2508.18507/) (2025)
- [Target Languages (vs. Inductive Biases) for Learning to Act and Plan](https://mlsanity.com/paper/2109.07195/) (2021)
- [Graph Learning for Numeric Planning](https://mlsanity.com/paper/2410.24080/) (2024)
- [Symmetry-Aware Transformer Training for Automated Planning](https://mlsanity.com/paper/2508.07743/) (2025)
- [Hierarchical Decomposition and Analysis for Generalized Planning](https://mlsanity.com/paper/2212.02823/) (2022)
- [Leveraging Action Relational Structures for Integrated Learning and Planning](https://mlsanity.com/paper/2504.20318/) (2025)
- [Exploiting Learned Policies in Focal Search](https://mlsanity.com/paper/2104.10535/) (2021)
- [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)
- [Per-Domain Generalizing Policies: On Validation Instances and Scaling Behavior](https://mlsanity.com/paper/2505.00439/) (2025)
- [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)
- [Sample-efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs](https://mlsanity.com/paper/2203.12679/) (2022)
- [A Differentiable Loss Function for Learning Heuristics in A*](https://mlsanity.com/paper/2209.05206/) (2022)
- [Exploration Policies for On-the-Fly Controller Synthesis: A Reinforcement Learning Approach](https://mlsanity.com/paper/2210.05393/) (2022)
- [Learning Efficiency Meets Symmetry Breaking](https://mlsanity.com/paper/2504.19738/) (2025)
- [Weisfeiler-Leman Features for Planning: A 1,000,000 Sample Size Hyperparameter Study](https://mlsanity.com/paper/2508.18515/) (2025)
- [On Policy Reuse: An Expressive Language for Representing and Executing General Policies that Call Other Policies](https://mlsanity.com/paper/2403.16824/) (2024)
- [Learning Generalized Policies for Fully Observable Non-Deterministic Planning Domains](https://mlsanity.com/paper/2404.02499/) (2024)
- [Learning to Ground Existentially Quantified Goals](https://mlsanity.com/paper/2409.20259/) (2024)

## Abstract (arXiv)

In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets). The ASNet is a neural network architecture that exploits the relational structure of (P)PDDL planning problems to learn a common set of weights that can be applied to any problem in a domain. By mimicking the actions chosen by a traditional, non-learning planner on a handful of small problems in a domain, ASNets are able to learn a generalised reactive policy that can quickly solve much larger instances from the domain. This work extends the ASNet architecture to make it more expressive, while still remaining invariant to a range of symmetries that exist in PPDDL problems. We also present a thorough experimental evaluation of ASNets, including a comparison with heuristic search planners on seven probabilistic and deterministic domains, an extended evaluation on over 18,000 Blocksworld instances, and an ablation study. Finally, we show that sparsity-inducing regularisation can produce ASNets that are compact enough for humans to understand, yielding insights into how the structure of ASNets allows them to generalise across a domain.