Lyceum: An Efficient and Scalable Ecosystem for Robot Learning
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.
Cite
Text
Summers et al. "Lyceum: An Efficient and Scalable Ecosystem for Robot Learning." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.Markdown
[Summers et al. "Lyceum: An Efficient and Scalable Ecosystem for Robot Learning." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.](https://mlanthology.org/l4dc/2020/summers2020l4dc-lyceum/)BibTeX
@inproceedings{summers2020l4dc-lyceum,
title = {{Lyceum: An Efficient and Scalable Ecosystem for Robot Learning}},
author = {Summers, Colin and Lowrey, Kendall and Rajeswaran, Aravind and Srinivasa, Siddhartha and Todorov, Emanuel},
booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control},
year = {2020},
pages = {793-803},
volume = {120},
url = {https://mlanthology.org/l4dc/2020/summers2020l4dc-lyceum/}
}