CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 深度强化学习
MGRL: Graph neural network based inference in a Markov network with Reinforcement Learning for visual navigation
Lu, Yi; Chen, Yaran; Zhao, Dongbin; Li, Dong
Source PublicationNeurocomputing
2021
Volume0Issue:0Pages:0
Abstract

Visual navigation is an essential task for indoor robots and usually uses the map as assistance to providing global information for the agent. Because the traditional maps match the environments, the map-based and map-building- based navigation methods are limited in the new environments for obtaining maps. Although the deep reinforcement learning navigation method, utilizing the non-map-based navigation technique, achieves satisfactory performance, it lacks the interpretability and the global view of the environment. Therefore, we propose a novel abstract map for the deep reinforcement learning navigation method with better global relative position information and more reasonable interpretability. The abstract map is modeled as a Markov network which is used for explicitly representing the regularity of objects arrangement, inuenced by people activities in different environments. Besides, a knowledge graph is utilized to initialize the structure of the Markov network, as providing the prior structure for the model and reducing the dificulty of model learning. Then, a graph neural network is adopted for probability inference in the Markov network. Furthermore, the update of the abstract map, including the knowledge graph structure and the parameters of the graph neural network, are combined into an end-to-end learning process trained by a reinforcement learning method. Finally, experiments in the AI2THOR framework and the physical environment indicate that our algorithm greatly improves the success rate of navigation in case of new environments, thus confirming the good generalization.

KeywordVisual navigation, graph neural network, Markov network, reinforcement learning, probabilistic graph model
DOIhttps://doi.org/10.1016/j.neucom.2020.07.091
Indexed BySCI
WOS IDWOS:000593102100012
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40610
Collection复杂系统管理与控制国家重点实验室_深度强化学习
复杂系统管理与控制国家重点实验室
Corresponding AuthorZhao, Dongbin
AffiliationInstitute of Automation, Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Lu, Yi,Chen, Yaran,Zhao, Dongbin,et al. MGRL: Graph neural network based inference in a Markov network with Reinforcement Learning for visual navigation[J]. Neurocomputing,2021,0(0):0.
APA Lu, Yi,Chen, Yaran,Zhao, Dongbin,&Li, Dong.(2021).MGRL: Graph neural network based inference in a Markov network with Reinforcement Learning for visual navigation.Neurocomputing,0(0),0.
MLA Lu, Yi,et al."MGRL: Graph neural network based inference in a Markov network with Reinforcement Learning for visual navigation".Neurocomputing 0.0(2021):0.
Files in This Item: Download All
File Name/Size DocType Version Access License
MGRL_Graph neural ne(976KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Lu, Yi]'s Articles
[Chen, Yaran]'s Articles
[Zhao, Dongbin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Lu, Yi]'s Articles
[Chen, Yaran]'s Articles
[Zhao, Dongbin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Lu, Yi]'s Articles
[Chen, Yaran]'s Articles
[Zhao, Dongbin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: MGRL_Graph neural network based inference in a Markov network with Reinforcement Learning for visual navigation.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.