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A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case
IEEE Access
◽
10.1109/access.2021.3053348
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2021
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Vol 9
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pp. 17084-17099
Author(s):
Samuel Yanes Luis
◽
Daniel Gutierrez Reina
◽
Sergio L. Toral Marin
Keyword(s):
Reinforcement Learning
◽
Path Planning
◽
Learning Approach
◽
Autonomous Surface Vehicles
Download Full-text
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IEEE Access
◽
10.1109/access.2019.2950166
◽
2019
◽
Vol 7
◽
pp. 159347-159356
Author(s):
Hamid Taghavifar
◽
Bin Xu
◽
Leyla Taghavifar
◽
Yechen Qin
Keyword(s):
Reinforcement Learning
◽
Path Planning
◽
Obstacle Avoidance
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Optimal Path
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Single Time
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Optimal Path Planning
◽
Dynamic Obstacle Avoidance
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Dynamic Obstacle
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A Model-free Deep Reinforcement Learning Approach for Robotic Manipulators Path Planning
10.23919/iccas52745.2021.9649802
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2021
◽
Author(s):
Wenxing Liu
◽
Hanlin Niu
◽
Muhammad Nasiruddin Mahyuddin
◽
Guido Herrmann
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Joaquin Carrasco
Keyword(s):
Reinforcement Learning
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Path Planning
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Robotic Manipulators
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Model Free
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UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach
GLOBECOM 2020 - 2020 IEEE Global Communications Conference
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10.1109/globecom42002.2020.9322234
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2020
◽
Author(s):
Harald Bayerlein
◽
Mirco Theile
◽
Marco Caccamo
◽
David Gesbert
Keyword(s):
Reinforcement Learning
◽
Path Planning
◽
Learning Approach
◽
Wireless Data
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Risk-Aware Multi-Agent Path Planning for Target Detection: A Multi-Agent Reinforcement Learning Approach
AIAA Scitech 2021 Forum
◽
10.2514/6.2021-0267
◽
2021
◽
Author(s):
Johnathan Votion
◽
Tao Feng
◽
Yongcan Cao
Keyword(s):
Reinforcement Learning
◽
Path Planning
◽
Target Detection
◽
Learning Approach
◽
Multi Agent
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A Deep Reinforcement Learning Approach for the Patrolling Problem of Water Resources Through Autonomous Surface Vehicles: The Ypacarai Lake Case
IEEE Access
◽
10.1109/access.2020.3036938
◽
2020
◽
Vol 8
◽
pp. 204076-204093
Author(s):
Samuel Yanes Luis
◽
Daniel Gutierrez Reina
◽
Sergio L. Toral Marin
Keyword(s):
Reinforcement Learning
◽
Water Resources
◽
Learning Approach
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Autonomous Surface Vehicles
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Service Chaining Offloading Decision in the EdgeAI: A Deep Reinforcement Learning Approach
2020 21st Asia-Pacific Network Operations and Management Symposium (APNOMS)
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10.23919/apnoms50412.2020.9237048
◽
2020
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Author(s):
Minkyun Lee
◽
Choong Seon Hong
Keyword(s):
Reinforcement Learning
◽
Learning Approach
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Emergent Control of MPSoC Operation by a Hierarchical Supervisor / Reinforcement Learning Approach
2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)
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10.23919/date48585.2020.9116574
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2020
◽
Author(s):
Florian Maurer
◽
Bryan Donyanavard
◽
Amir M. Rahmani
◽
Nikil Dutt
◽
Andreas Herkersdorf
Keyword(s):
Reinforcement Learning
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Energy-efficient UAV trajectory design for backscatter communication: A deep reinforcement learning approach
China Communications
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10.23919/jcc.2020.10.009
◽
2020
◽
Vol 17
(10)
◽
pp. 129-141
Author(s):
Yiwen Nie
◽
Junhui Zhao
◽
Jun Liu
◽
Jing Jiang
◽
Ruijin Ding
Keyword(s):
Reinforcement Learning
◽
Energy Efficient
◽
Learning Approach
◽
Trajectory Design
◽
Backscatter Communication
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Policy based reinforcement learning approach Of Jobshop scheduling with high level deadlock detection
10.31274/etd-180810-1488
◽
2014
◽
Author(s):
Mengmeng Chen
Keyword(s):
Reinforcement Learning
◽
Learning Approach
◽
Deadlock Detection
◽
Jobshop Scheduling
◽
High Level
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Author response for "Deep learning and reinforcement learning approach on microgrid"
10.1002/2050-7038.12531/v2/response1
◽
2020
◽
Author(s):
Kumar Chandrasekaran
◽
Prabaakaran Kandasamy
◽
Srividhya Ramanathan
Keyword(s):
Deep Learning
◽
Reinforcement Learning
◽
Author Response
◽
Learning Approach
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