论文标题
Lyceum：机器人学习的高效且可扩展的生态系统
Lyceum: An efficient and scalable ecosystem for robot learning
论文作者
论文摘要
我们介绍了Lyceum，这是一种用于机器人学习的高性能计算生态系统。 Lyceum建立在朱莉娅编程语言和穆乔科物理模拟器之上，将高级编程语言的易用性与本机C的性能相结合，此外，Lyceum还具有直接的API，可以支持多个核心和机器的平行计算。总体而言，根据环境的复杂性，Lyceum的速度比Openai的健身房和DeepMind的DM-Control（例如Openai的健身房）快5-30倍。这大大减少了各种增强学习算法的训练时间；并且也足够快，可以通过Mujoco支持实时模型预测控制。代码，教程和演示视频可以在以下网址找到：www.lyceum.ml。
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.
