Lyceum: An efficient and scalable ecosystem for robot learning

Autor:	Summers, Colin, Lowrey, Kendall, Rajeswaran, Aravind, Srinivasa, Siddhartha, Todorov, Emanuel
Rok vydání:	2020
Předmět:	Computer Science - Robotics Computer Science - Artificial Intelligence Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.07343 Zobrazit plný text záznamu View this record from Arxiv