Reinforcement learning from expert demonstrations with application to redundant robot control. (March 2023)

• Record Type: Journal Article
• Authors: Ramirez, Jorge; Yu, Wen
• Is Part Of: Engineering applications of artificial intelligence. Volume 119(2023)
• Publication Date: 2023-03
• Subjects: Reinforcement learning -- Expert demonstrations -- Biased exploration -- Robot manipulator; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105753
• Languages: English
• ISSNs: 0952-1976

Abstract

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.