Parallel Gym Gazebo: a Scalable Parallel Robot Deep Reinforcement Learning Platform

Zhen Liang, Zhongxuan Cai, Minglong Li, Wenjing Yang

Abstract: Deep reinforcement learning is making advances in robotics with the platforms of realistic environment simulation. However, as shown in this paper, the realistic simulation introduces vast time cost which is the bottleneck of the learning procedure. To solve this problem generally, we propose a parallel reinforcement learning platform which follows the master-slave principle and integrates learning programs with multiple distributedrobot simulators. The platform is intrinsically scalable and requires no modification to existing serially designed learning environments or algorithms. Experimental results demonstrate that our platform significantly accelerates the learning progress of robots, in direct proportion to the parallel scale. The parallelism also brings richer exploration and sampling, enhancing the performance of deep reinforcement learning algorithms compared with existing serial platforms.