GzDRL: Reproducible and Scalable Deep Reinforcement Learning with Gazebo
A new framework accelerates robotics research by eliminating nondeterministic bottlenecks in simulation environments.
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 ir...
ArXiv CS.RO
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GzDRL addresses a critical reproducibility crisis in robotics research by removing the nondeterministic overhead of traditional middleware integration. This innovation allows for consistent, high-speed reinforcement learning training cycles that are essential for bridging the gap between simulation and real-world deployment.
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