Data-Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control

The learning inefficiency of reinforcement learning (RL) from scratch hinders its practical application toward continuous robotic tracking control, especially for high-dimensional robots. This article proposes a data-informed residual reinforcement learning (DR-RL)-based robotic tracking control scheme applicable to robots with high dimensionality. The proposed DR-RL methodology outperforms common RL methods regarding sample efficiency and scalability. Specifically, we first decouple the original robot into low-dimensional robotic subsystems; and further utilize one-step backward data to construct incremental subsystems that are equivalent model-free representations of the aforementioned decoupled robotic subsystems. The formulated incremental subsystems allow for parallel learning to relieve computation load and offer us mathematical descriptions of robotic movements for conducting theoretical analysis …     «

The learning inefficiency of reinforcement learning (RL) from scratch hinders its practical application toward continuous robotic tracking control, especially for high-dimensional robots. This article proposes a data-informed residual reinforcement learning (DR-RL)-based robotic tracking control scheme applicable to robots with high dimensionality. The proposed DR-RL methodology outperforms common RL methods regarding sample efficiency and scalability. Specifically, we first decouple the origina...     »