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- W3136347463 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." @default.
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- W3136347463 date "2021-03-08" @default.
- W3136347463 modified "2023-10-18" @default.
- W3136347463 title "Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learning." @default.
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