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, Bhuditama, Tan, Ah-Hwee
Other Authors: School of Electrical and Electronic Engineering
Format: Conference Paper
Language:English
Published: 2021
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Multi-agent Exploration
Deep Learning
Online Access:https://hdl.handle.net/10356/148510

Internet

https://hdl.handle.net/10356/148510

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