# Deep Q-Learning for Directed Acyclic Graph Generation

arXiv: https://arxiv.org/abs/1906.02280 · HTML: https://mlsanity.com/paper/1906.02280/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Heterogeneous Graph Generation: A Hierarchical Approach using Node Feature Pooling](https://mlsanity.com/paper/2410.11972/) (2024)

## Abstract (arXiv)

We present a method to generate directed acyclic graphs (DAGs) using deep reinforcement learning, specifically deep Q-learning. Generating graphs with specified structures is an important and challenging task in various application fields, however most current graph generation methods produce graphs with undirected edges. We demonstrate that this method is capable of generating DAGs with topology and node types satisfying specified criteria in highly sparse reward environments.