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About this paper
Michael Kaup, Cornelius Wolff, Hyerim Hwang, Julius Mayer, Elia Bruni
This paper reviews nine popular physics engines used in reinforcement learning research, including MuJoCo, PyBullet, and Unity, to guide researchers in selecting appropriate simulation tools. It provides a detailed comparison of their popularity, features, usability, and capabilities for single-agent and multi-agent reinforcement learning, highlighting prevalent trade-offs between performance and user-friendliness.
Key insights
- A significant trade-off exists between physics engine performance and usability, where the most performant engines often have poor usability, and user-friendly engines tend to suffer from poor performance.
- There is a persistent need for improved tools and libraries for custom creation of complex environments and for pre-built solutions that extend beyond basic benchmark tasks.
- Rigorous, quantitative performance comparisons for Multi-Agent Reinforcement Learning (MARL) in complex 3D environments across various engines are largely absent in current research.
Summary by alphaXiv.
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