We introduce Lyceum, a high-performance computational ecosystem for robot learning. Lyceum is built on top of the Julia programming language and the MuJoCo physics simulator, combining the ease-of-use of a high-level programming language with the performance of native C. In addition, Lyceum has a straightforward API to support parallel computation across multiple cores and machines. Overall, depending on the complexity of the environment, Lyceum is 5-30x faster compared to other popular abstractions like OpenAI's Gym and DeepMind's dm-control. This substantially reduces training time for various reinforcement learning algorithms; and is also fast enough to support real-time model predictive control through MuJoCo. The code, tutorials, and demonstration videos can be found at: www.lyceum.ml.

中文翻译：

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Lyceum：一个高效且可扩展的机器人学习生态系统

我们介绍了 Lyceum，一个用于机器人学习的高性能计算生态系统。Lyceum 建立在 Julia 编程语言和 MuJoCo 物理模拟器之上，结合了高级编程语言的易用性和原生 C 的性能。此外，Lyceum 有一个简单的 API 来支持跨平台的并行计算多核和机器。总体而言，根据环境的复杂性，与 OpenAI 的 Gym 和 DeepMind 的 dm-control 等其他流行抽象相比，Lyceum 的速度要快 5-30 倍。这大大减少了各种强化学习算法的训练时间；并且还足够快，可以通过 MuJoCo 支持实时模型预测控制。代码、教程和演示视频可在以下网址找到：www.lyceum.ml。