MGRL: Graph neural network based inference in a Markov network with Reinforcement Learning for visual navigation

doi:https://doi.org/10.1016/j.neucom.2020.07.091

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 Publication	Neurocomputing
	2021
Volume	0 Issue:0 Pages: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.
Keyword	Visual navigation, graph neural network, Markov network, reinforcement learning, probabilistic graph model
DOI	https://doi.org/10.1016/j.neucom.2020.07.091
Indexed By	SCI
WOS ID	WOS:000593102100012
Citation statistics
Document Type	期刊论文
Identifier	http://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 Author	Zhao, Dongbin
Affiliation	Institute of Automation, Chinese Academy of Sciences
First Author Affilication	Institute of Automation, Chinese Academy of Sciences
Corresponding Author Affilication	Institute 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.

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