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Dragonfly: a modular deep reinforcement learning library

Machine Learning 2025-07-29 v2

Abstract

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.

Keywords

Cite

@article{arxiv.2505.03778,
  title  = {Dragonfly: a modular deep reinforcement learning library},
  author = {Jonathan Viquerat and Paul Garnier and Amirhossein Bateni and Elie Hachem},
  journal= {arXiv preprint arXiv:2505.03778},
  year   = {2025}
}
R2 v1 2026-06-28T23:23:24.247Z