End-to-end Deep Reinforcement Learning for Multi-agent Collaborative Exploration-Reference-Cited by-同舟云学术

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End-to-end Deep Reinforcement Learning for Multi-agent Collaborative Exploration

Published:2019-10 Issue: Volume: Page:
ISSN:
Container-title:2019 IEEE International Conference on Agents (ICA)
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Author:

Chen Zichen,Subagdja Budhitama,Tan Ah-Hwee

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8922814/8929116/08929192.pdf?arnumber=8929192

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MAexp: A Generic Platform for RL-based Multi-Agent Exploration;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Digital Twin-Based Transfer Learning for Collaborative Robot Systems: A Proof of Concept;2024 International Conference on Information Networking (ICOIN);2024-01-17

3. Toward Generalized Artificial Intelligence by Assessment Aggregation With Applications to Standard and Extreme Classifications;IEEE Transactions on Neural Networks and Learning Systems;2024

4. Autonomous Exploration and Mapping for Mobile Robots via Cumulative Curriculum Reinforcement Learning;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

5. Bio-inspired Decentralized Multi-robot Exploration in Unknown Environments;2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS);2023-09-25

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