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

• 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
• Institution: Singapore Management 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...