Curiosity model policy optimization for robotic manipulator tracking control with input saturation in uncertain environment

Autor: Tu Wang, Fujie Wang, Zhongye Xie, Feiyan Qin
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Frontiers in Neurorobotics, Vol 18 (2024)
Druh dokumentu: article
ISSN: 1662-5218
DOI: 10.3389/fnbot.2024.1376215
Popis: In uncertain environments with robot input saturation, both model-based reinforcement learning (MBRL) and traditional controllers struggle to perform control tasks optimally. In this study, an algorithmic framework of Curiosity Model Policy Optimization (CMPO) is proposed by combining curiosity and model-based approach, where tracking errors are reduced via training agents on control gains for traditional model-free controllers. To begin with, a metric for judging positive and negative curiosity is proposed. Constrained optimization is employed to update the curiosity ratio, which improves the efficiency of agent training. Next, the novelty distance buffer ratio is defined to reduce bias between the environment and the model. Finally, CMPO is simulated with traditional controllers and baseline MBRL algorithms in the robotic environment designed with non-linear rewards. The experimental results illustrate that the algorithm achieves superior tracking performance and generalization capabilities.
Databáze: Directory of Open Access Journals