# Generalized Planning With Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2005.02305 · HTML: https://mlsanity.com/paper/2005.02305/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2020.

## Built on by (most cited first, 30 of 35)

- [Generalized Planning in PDDL Domains with Pretrained Large Language Models](https://mlsanity.com/paper/2305.11014/) (2023)
- [TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs](https://mlsanity.com/paper/2207.05006/) (2022)
- [Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning](https://mlsanity.com/paper/2105.14074/) (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)
- [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)
- [A Research Agenda for AI Planning in the Field of Flexible Production Systems](https://mlsanity.com/paper/2112.15484/) (2021)
- [PG3: Policy-Guided Planning for Generalized Policy Generation](https://mlsanity.com/paper/2204.10420/) (2022)
- [Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability](https://mlsanity.com/paper/2109.11792/) (2021)
- [Symmetries and Expressive Requirements for Learning General Policies](https://mlsanity.com/paper/2409.15892/) (2024)
- [General Policies, Subgoal Structure, and Planning Width](https://mlsanity.com/paper/2311.05490/) (2023)
- [Unlocking Large Language Model's Planning Capabilities with Maximum Diversity Fine-tuning](https://mlsanity.com/paper/2406.10479/) (2024)
- [On Using Admissible Bounds for Learning Forward Search Heuristics](https://mlsanity.com/paper/2308.11905/) (2023)
- [LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying](https://mlsanity.com/paper/2308.13542/) (2023)
- [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)
- [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)
- [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)
- [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)
- [Scaling up ML-based Black-box Planning with Partial STRIPS Models](https://mlsanity.com/paper/2207.04479/) (2022)
- [Meta-operators for Enabling Parallel Planning Using Deep Reinforcement Learning](https://mlsanity.com/paper/2403.08910/) (2024)
- [Scaling Up without Fading Out: Goal-Aware Sparse GNN for RL-based Generalized Planning](https://mlsanity.com/paper/2508.10747/) (2025)
- [Integrating Symbolic RL Planning into a BDI-based Autonomous UAV Framework: System Integration and SIL Validation](https://mlsanity.com/paper/2508.11890/) (2025)
- [LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems](https://mlsanity.com/paper/2508.13371/) (2025)

## Abstract (arXiv)

A hallmark of intelligence is the ability to deduce general principles from examples, which are correct beyond the range of those observed. Generalized Planning deals with finding such principles for a class of planning problems, so that principles discovered using small instances of a domain can be used to solve much larger instances of the same domain. In this work we study the use of Deep Reinforcement Learning and Graph Neural Networks to learn such generalized policies and demonstrate that they can generalize to instances that are orders of magnitude larger than those they were trained on.