Dragonfly: a modular deep reinforcement learning library
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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.
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.
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4 of 11 references.
References · 4
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
- 2018Learning to Drive in a Daycited by 273
- 2017Asymmetric Actor Critic for Image-Based Robot Learningcited by 238
- 2024Beacon, a lightweight deep reinforcement learning benchmark library for flow controlcited by 3
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- 2025AgentFly: Extensible and Scalable Reinforcement Learning for LM Agents
- 2024VisFly: An Efficient and Versatile Simulator for Training Vision-based Flight
- 2026Insect-inspired modular architectures as inductive biases for reinforcement learning
- 2022CORL: Research-oriented Deep Offline Reinforcement Learning Library
- 2025RSL-RL: A Learning Library for Robotics Research
- 2025NiceWebRL: a Python library for human subject experiments with reinforcement learning environments
- 2026Helix: Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving
- 2022GriddlyJS: A Web IDE for Reinforcement Learning