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.
翻译:我们引入了高性能计算系统Lyceum,这是机器人学习的高性能计算生态系统。Lyceum建在Julia编程语言和Mujoco物理模拟器之上,将高水平编程语言的简单使用与本地C的性能结合起来。此外,Lyceum有一个直截了当的API支持多个核心和机器的平行计算。总的说来,根据环境的复杂性,Lyceum比OpenAI的 Gym 和 DeepMind的 dm- control等其他流行的抽象模型更快5-30x。这大大缩短了各种强化学习算法的培训时间;还足够快,足以支持通过Mujoco实时模型的预测控制。代码、辅导和演示视频可以在www.lyceum.ml上找到。