# Dragonfly: a modular deep reinforcement learning library

arXiv: https://arxiv.org/abs/2505.03778 · HTML: https://mlsanity.com/paper/2505.03778/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Learning to Drive in a Day](https://mlsanity.com/paper/1807.00412/) (2018)
- [Asymmetric Actor Critic for Image-Based Robot Learning](https://mlsanity.com/paper/1710.06542/) (2017)
- [Beacon, a lightweight deep reinforcement learning benchmark library for flow control](https://mlsanity.com/paper/2402.17402/) (2024)

## Abstract (arXiv)

Dragonfly is a deep reinforcement learning library focused on modularity, in order to ease experimentation and developments. It relies on a json serialization that allows to swap building blocks and perform parameter sweep, while minimizing code maintenance. Some of its features are specifically designed for CPU-intensive environments, such as numerical simulations. Its performance on standard agents using common benchmarks compares favorably with the literature.