Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learning
This work addresses the need for faster simulations to reduce training time in robotics and RL, but it is incremental as it focuses on benchmarking existing environments.
The paper compares four popular simulation environments for robotics and reinforcement learning using benchmarks based on industrial applications, finding that they benefit most from single-core performance but can leverage multi-core systems for parallel simulations to improve speed.
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.