Lyceum: An efficient and scalable ecosystem for robot learning

Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha Srinivasa, Emanuel Todorov

Abstract: 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.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/lyceum-an-efficient-and-scalable-ecosystem/code)