# RLgraph: Modular Computation Graphs for Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1810.09028 · HTML: https://mlsanity.com/paper/1810.09028/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2018.

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

- [Launchpad: A Programming Model for Distributed Machine Learning Research](https://mlsanity.com/paper/2106.04516/) (2021)

## Abstract (arXiv)

Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns. We argue for the separation of logical component composition, backend graph definition, and distributed execution. To this end, we introduce RLgraph, a library for designing and executing reinforcement learning tasks in both static graph and define-by-run paradigms. The resulting implementations are robust, incrementally testable, and yield high performance across different deep learning frameworks and distributed backends.