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

• Main Authors: Chen, Zichen, Subagdja, Bhuditama, Tan, Ah-Hwee
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• 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
• Institution: Nanyang Technological University

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...