GzDRL: Reproducible and Scalable Deep Reinforcement Learning with Gazebo
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...
| Authors | Amal Dev Haridevan, Junjie Kang, Jinjun Shan |
|---|---|
| Primary category | cs.RO |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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