Lyceum：高速でスケーラブルなロボット学習エコシステム
Lyceum: An efficient and scalable ecosystem for robot learning
JuliaとMuJoCo上に構築したロボット学習向けの高速計算基盤で、既存のGymやdm-controlより5〜30倍速く、並列計算やリアルタイムMPCも可能にした。
著者: Colin Summers, Kendall Lowrey, Aravind Rajeswaran, Siddhartha Srinivasa, Emanuel Todorov
分類: cs.RO, cs.AI, cs.LG, cs.SY, eess.SY
原文アブストラクト
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.
