← Back to papers
2020 L4DC L4DC 2020

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.

🚀 Conference Pioneer — L4DC 2020
🌉 Interdisciplinary Bridge — Computer Science and Machine Learning and Robotics
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics
🐣 Hot Topic Early Bird — robot learning

Authors

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

Topics

Reinforcement Learning Robotics Robotics > Systems > Control Systems Computer Science > Systems > Distributed Systems Machine Learning > Learning Types > Reinforcement Learning

Keywords

reinforcement learning model predictive control robot learning parallel computation physics simulation physics simulator
Download PDF

Related papers

Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach 2020
Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models 2020
Learning solutions to hybrid control problems using Benders cuts 2020
A Finite-Sample Deviation Bound for Stable Autoregressive Processes 2020
Constrained Upper Confidence Reinforcement Learning 2020