Lyceum: An efficient and scalable ecosystem for robot learning
This provides a more efficient and scalable tool for researchers and practitioners in robotics and reinforcement learning, though it is incremental as it builds on existing technologies.
The authors tackled the problem of slow training times in robot learning by introducing Lyceum, a high-performance computational ecosystem built on Julia and MuJoCo, which achieves 5-30x faster performance compared to existing frameworks like OpenAI's Gym and DeepMind's dm-control.
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.