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

• URL: http://arxiv.org/abs/2103.04616v1
• Date: Mon, 8 Mar 2021 09:08:53 GMT
• Title: Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learning
• Authors: Marian K\"orber, Johann Lange, Stephan Rediske, Simon Steinmann, Roland Gl\"uck
• Abstract summary: We show that the chosen simulation environments benefit the most from single core performance. Using a multi core system, multiple simulations could be run in parallel to increase the performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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