# Action Schema Networks: Generalised Policies with Deep Learning

arXiv: https://arxiv.org/abs/1709.04271 · HTML: https://mlsanity.com/paper/1709.04271/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2017.

## Built on by (most cited first, 30 of 32)

- [Learning First-Order Representations for Planning from Black-Box States: New Results](https://mlsanity.com/paper/2105.10830/) (2021)
- [Reinforcement learning on graphs: A survey](https://mlsanity.com/paper/2204.06127/) (2022)
- [Explore to Generalize in Zero-Shot RL](https://mlsanity.com/paper/2306.03072/) (2023)
- [Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning](https://mlsanity.com/paper/2403.16508/) (2024)
- [Learning General Policies from Small Examples Without Supervision](https://mlsanity.com/paper/2101.00692/) (2021)
- [Language Models For Generalised PDDL Planning: Synthesising Sound and Programmatic Policies](https://mlsanity.com/paper/2508.18507/) (2025)
- [Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects](https://mlsanity.com/paper/2301.11087/) (2023)
- [Granger Causal Interaction Skill Chains](https://mlsanity.com/paper/2306.09509/) (2023)
- [General Policies, Subgoal Structure, and Planning Width](https://mlsanity.com/paper/2311.05490/) (2023)
- [On Using Admissible Bounds for Learning Forward Search Heuristics](https://mlsanity.com/paper/2308.11905/) (2023)
- [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)
- [Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planning](https://mlsanity.com/paper/2211.13316/) (2022)
- [PDDLFuse: A Tool for Generating Diverse Planning Domains](https://mlsanity.com/paper/2411.19886/) (2024)
- [Per-Domain Generalizing Policies: On Validation Instances and Scaling Behavior](https://mlsanity.com/paper/2505.00439/) (2025)
- [Meta-Reinforcement Learning for Heuristic Planning](https://mlsanity.com/paper/2107.02603/) (2021)
- [Hierarchical Reinforcement Learning with AI Planning Models](https://mlsanity.com/paper/2203.00669/) (2022)
- [Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems](https://mlsanity.com/paper/2204.04301/) (2022)
- [A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making](https://mlsanity.com/paper/2304.10590/) (2023)
- [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)
- [WLPlan: Relational Features for Symbolic Planning](https://mlsanity.com/paper/2411.00577/) (2024)
- [Learning to Select Goals in Automated Planning with Deep-Q Learning](https://mlsanity.com/paper/2406.14779/) (2024)
- [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)
- [LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems](https://mlsanity.com/paper/2508.13371/) (2025)
- [Learning General Policies From Examples](https://mlsanity.com/paper/2509.02794/) (2025)
- [Textual Planning with Explicit Latent Transitions](https://mlsanity.com/paper/2602.04557/) (2026)
- [On Sample-Efficient Generalized Planning via Learned Transition Models](https://mlsanity.com/paper/2602.23148/) (2026)

## Abstract (arXiv)

In this paper, we introduce the Action Schema Network (ASNet): a neural network architecture for learning generalised policies for probabilistic planning problems. By mimicking the relational structure of planning problems, ASNets are able to adopt a weight-sharing scheme which allows the network to be applied to any problem from a given planning domain. This allows the cost of training the network to be amortised over all problems in that domain. Further, we propose a training method which balances exploration and supervised training on small problems to produce a policy which remains robust when evaluated on larger problems. In experiments, we show that ASNet's learning capability allows it to significantly outperform traditional non-learning planners in several challenging domains.