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This letter compares the performance of four different, popular simulationenvironments for robotics and reinforcement learning (RL) through a series ofbenchmarks. The benchmarked scenarios are designed carefully with currentindustrial applications in mind. Given the need to run simulations as fast aspossible to reduce the real-world training time of the RL agents, thecomparison includes not only different simulation environments but alsodifferent hardware configurations, ranging from an entry-level notebook up to adual CPU high performance server. We show that the chosen simulationenvironments benefit the most from single core performance. Yet, using a multicore system, multiple simulations could be run in parallel to increase theperformance.

Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learning