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Human skill knowledge guided global trajectory policy reinforcement learning method.

Authors :
Yajing Zang
Pengfei Wang
Fusheng Zha
Wei Guo
Chuanfeng Li
Lining Sun
Source :
Frontiers in Neurorobotics; 2024, p1-12, 12p
Publication Year :
2024

Abstract

Traditional trajectory learningmethods based on Imitation Learning (IL) only learn the existing trajectory knowledge from human demonstration. In this way, it can not adapt the trajectory knowledge to the task environment by interacting with the environment and fine-tuning the policy. To address this problem, a global trajectory learning method which combinines IL with Reinforcement Learning (RL) to adapt the knowledge policy to the environment is proposed. In this paper, IL is proposed to acquire basic trajectory skills, and then learns the agent will explore and exploit more policy which is applicable to the current environment by RL. The basic trajectory skills include the knowledge policy and the time stage information in the whole task space to help learn the time series of the trajectory, and are used to guide the subsequent RL process. Notably, neural networks are not used to model the action policy and the Q value of RL during the RL process. Instead, they are sampled and updated in the whole task space and then transferred to the networks after the RL process through Behavior Cloning (BC) to get continuous and smooth global trajectory policy. The feasibility and the effectiveness of the method was validated in a custom Gym environment of a flower drawing task. And then, we executed the learned policy in the real-world robot drawing experiment. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16625218
Database :
Complementary Index
Journal :
Frontiers in Neurorobotics
Publication Type :
Academic Journal
Accession number :
176378561
Full Text :
https://doi.org/10.3389/fnbot.2024.1368243