# Reinforcement learning on graphs: A survey

arXiv: https://arxiv.org/abs/2204.06127 · HTML: https://mlsanity.com/paper/2204.06127/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 102 of its 222 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 30 of 102)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Diversity is All You Need: Learning Skills without a Reward Function](https://mlsanity.com/paper/1802.06070/) (2018)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [Data-Efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1805.08296/) (2018)
- [Parameterized Explainer for Graph Neural Network](https://mlsanity.com/paper/2011.04573/) (2020)
- [Adversarial Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/1805.07984/) (2018)
- [MolGAN: An implicit generative model for small molecular graphs](https://mlsanity.com/paper/1805.11973/) (2018)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)

## Built on by (most cited first, 20 of 20)

- [Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review](https://mlsanity.com/paper/2303.06471/) (2023)
- [The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models](https://mlsanity.com/paper/2402.01874/) (2024)
- [Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing](https://mlsanity.com/paper/2402.05027/) (2024)
- [Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection](https://mlsanity.com/paper/2312.05526/) (2023)
- [Graph Reinforcement Learning for Power Grids: A Comprehensive Survey](https://mlsanity.com/paper/2407.04522/) (2024)
- [A Comprehensive Survey on Graph Summarization with Graph Neural Networks](https://mlsanity.com/paper/2302.06114/) (2023)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?](https://mlsanity.com/paper/2311.13414/) (2023)
- [Agentic-KGR: Co-evolutionary Knowledge Graph Construction through Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2510.09156/) (2025)
- [TARN-VIST: Topic Aware Reinforcement Network for Visual Storytelling](https://mlsanity.com/paper/2403.11550/) (2024)
- [Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labels](https://mlsanity.com/paper/2407.05934/) (2024)
- [Dynamic Weight Adjusting Deep Q-Networks for Real-Time Environmental Adaptation](https://mlsanity.com/paper/2411.02559/) (2024)
- [A Graph-Based Reinforcement Learning Approach with Frontier Potential Based Reward for Safe Cluttered Environment Exploration](https://mlsanity.com/paper/2504.11907/) (2025)
- [Metric spaces of walks and Lipschitz duality on graphs](https://mlsanity.com/paper/2508.19709/) (2025)
- [Leveraging Vulnerabilities in Temporal Graph Neural Networks via Strategic High-Impact Assaults](https://mlsanity.com/paper/2509.25418/) (2025)
- [Platform-Agnostic Reinforcement Learning Framework for Safe Exploration of Cluttered Environments with Graph Attention](https://mlsanity.com/paper/2511.15358/) (2025)
- [Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulators](https://mlsanity.com/paper/2601.16866/) (2026)
- [EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly](https://mlsanity.com/paper/2605.18872/) (2026)
- [Projecting Latent RL Actions: Towards Generalizable and Scalable Graph Combinatorial Optimization](https://mlsanity.com/paper/2605.19721/) (2026)
- [HiGFRL: Hierarchical Graph Fusion-Driven Reinforcement Learning for Dependency-Aware Task Scheduling in Heterogeneous Cloud](https://mlsanity.com/paper/2609.14968/) (2026)

## Abstract (arXiv)

Graph mining tasks arise from many different application domains, ranging from social networks, transportation to E-commerce, etc., which have been receiving great attention from the theoretical and algorithmic design communities in recent years, and there has been some pioneering work employing the research-rich Reinforcement Learning (RL) techniques to address graph data mining tasks. However, these graph mining methods and RL models are dispersed in different research areas, which makes it hard to compare them. In this survey, we provide a comprehensive overview of RL and graph mining methods and generalize these methods to Graph Reinforcement Learning (GRL) as a unified formulation. We further discuss the applications of GRL methods across various domains and summarize the method descriptions, open-source codes, and benchmark datasets of GRL methods. Furthermore, we propose important directions and challenges to be solved in the future. As far as we know, this is the latest work on a comprehensive survey of GRL, this work provides a global view and a learning resource for scholars. In addition, we create an online open-source for both interested scholars who want to enter this rapidly developing domain and experts who would like to compare GRL methods.