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