Reinforced iLQR: A Sample-Efficient Robot Locomotion Learning
Tongyu Zong,Liyang Sun,Yong Liu,Tongyu Zong,Liyang Sun,Yong Liu
Robot locomotion is a major challenge in robotics. Model-based approaches are vulnerable to model errors, and incur high computation overhead resulted from long control horizon. Model-free approaches are trained with a large number of training samples, which are expensive to obtain. In this paper, we develop a hybrid control and learning framework, called Reinforced iLQR (RiLQR), which combines th...
