University of Bahrain
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Navigation of Robotic-Arms using Policy Gradient Reinforcement Learning

Show simple item record Farag, Wael 2022-03-09T06:14:37Z 2022-03-09T06:14:37Z 2022-03-09
dc.identifier.issn 2210-142X
dc.description.abstract In this paper, the Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm is employed to enable a double-jointed robot arm to reach continuously-changing target locations. The experimentation of the algorithm is carried out by training an agent to control the movement of this double-jointed robot arm. The architectures of the actor and critic networks are meticulously designed and the DDPG hyperparameters are carefully tuned. An enhanced version of the DDPG is also presented to handle multiple robot arms simultaneously. The trained agents are successfully tested in the Unity Machine Learning Agents environment for controlling both a single robot arm as well as multiple simultaneous robot arms. The testing shows the robust performance of the DDPG algorithm for empowering robot arm maneuvering in complex environments. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject Reinforcement Learning en_US
dc.subject Policy-Gradients Methods en_US
dc.subject DDPG, Machine Learning en_US
dc.subject Robotics en_US
dc.subject Robot Arm en_US
dc.title Navigation of Robotic-Arms using Policy Gradient Reinforcement Learning en_US
dc.identifier.doi en_US
dc.volume 11 en_US
dc.issue 1 en_US
dc.pagestart XXXX en_US
dc.pageend XXXX en_US
dc.contributor.authoraffiliation College of Eng. & Tech., American University of the Middle East, Kuwait. en_US
dc.contributor.authoraffiliation Electrical Eng. Dept., Cairo University, Egypt. en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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