End-to-end deep reinforcement learning for multi-agent collaborative exploration

End-to-end deep reinforcement learning for multi-agent collaborative exploration

Exploring an unknown environment by multiple autonomous robots is a major challenge in robotics domains. As multiple robots are assigned to explore different locations, they may interfere each other making the overall tasks less efficient. In this paper, we present a new model called CNN-based Multi...

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Bibliographic Details
Main Authors:	CHEN, Zichen, SUBAGDJA, Budhitama, TAN, Ah-hwee
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2019
Subjects:	Deep learning Multi-agent exploration Reinforcement Learning Artificial Intelligence and Robotics Databases and Information Systems
Online Access:	https://ink.library.smu.edu.sg/sis_research/6170 https://ink.library.smu.edu.sg/context/sis_research/article/7173/viewcontent/Observation_based_Deep_Reinforcement_Learning_for_Multi_agent_Collaborative_Exploration.pdf
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Institution:	Singapore Management University
Language:	English

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