Reinforcement learning from expert demonstrations with application to redundant robot control. (March 2023) - Ramirez, Jorge; Yu, Wen
Abstract: Current methods of reinforcement learning from expert demonstrations require humans to give all possible demonstrations in the learning phase, which is very difficult for continuous or high-dimensional spaces. In this paper, we proposed biased exploration reinforcement learning to avoid the exploration of unnecessary states and actions of the expert demonstrations. We present a convergence analysis of the novel method. This method is applied to learn the control of a redundant robot manipulator with 7-degree-of-freedom. The experimental results demonstrate that the proposed method accelerates the learning phase. The obtained policy can successfully achieve the pretended task.