[Submitted on 3 Sep 2026]
Title:GzDRL: Reproducible and Scalable Deep Reinforcement Learning with Gazebo
View a PDF of the paper titled GzDRL: Reproducible and Scalable Deep Reinforcement Learning with Gazebo, by Amal Dev Haridevan and 2 other authors
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Abstract:We present GzDRL, a novel single-process reinforcement learning (RL) framework for Gazebo that overcomes longstanding bottlenecks in scalable, reproducible robotics experimentation. Unlike conventional middleware-based RL-Gazebo integrations that suffer from nondeterminism and irreproducibility, GzDRL introduces a systematic, middleware-free environment-stepping mechanism that directly synchronizes agent actions and physics updates. This design enables deterministic, high-throughput data collection, efficient vectorization, and reproducible RL training and evaluation. Comprehensive benchmarks demonstrate that GzDRL achieves the highest workstation throughput among the evaluated frameworks while remaining competitive with GPU-accelerated simulators on laptop hardware, and maintains precise agent-environment synchronization, multi-agent scalability, and experiment-level reproducibility. We further validate sim-to-real transfer by deploying learned policies directly onto a physical quadrotor, without fine-tuning. Our results establish GzDRL as an accessible and reproducible platform for advancing RL in robotics and automation.
Comments: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2609.13243 [cs.RO]
(or arXiv:2609.13243v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.13243
arXiv-issued DOI via DataCite
Submission history
From: Amaldev Haridevan [view email] [v1] Thu, 3 Sep 2026 17:35:13 UTC (8,400 KB)
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