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Article

A Deep Reinforcement Learning Algorithm Based on Tetanic Stimulation and Amnesic Mechanisms for Continuous Control of Multi-DOF Manipulator

College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
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Author to whom correspondence should be addressed.
Academic Editors: Ioan Doroftei and Karsten Berns
Actuators 2021, 10(10), 254; https://doi.org/10.3390/act10100254
Received: 11 August 2021 / Revised: 14 September 2021 / Accepted: 20 September 2021 / Published: 29 September 2021
(This article belongs to the Special Issue Advanced Robots: Design, Control and Application)
Deep Reinforcement Learning (DRL) has been an active research area in view of its capability in solving large-scale control problems. Until presently, many algorithms have been developed, such as Deep Deterministic Policy Gradient (DDPG), Twin-Delayed Deep Deterministic Policy Gradient (TD3), and so on. However, the converging achievement of DRL often requires extensive collected data sets and training episodes, which is data inefficient and computing resource consuming. Motivated by the above problem, in this paper, we propose a Twin-Delayed Deep Deterministic Policy Gradient algorithm with a Rebirth Mechanism, Tetanic Stimulation and Amnesic Mechanisms (ATRTD3), for continuous control of a multi-DOF manipulator. In the training process of the proposed algorithm, the weighting parameters of the neural network are learned using Tetanic stimulation and Amnesia mechanism. The main contribution of this paper is that we show a biomimetic view to speed up the converging process by biochemical reactions generated by neurons in the biological brain during memory and forgetting. The effectiveness of the proposed algorithm is validated by a simulation example including the comparisons with previously developed DRL algorithms. The results indicate that our approach shows performance improvement in terms of convergence speed and precision. View Full-Text
Keywords: multi-DOF manipulator; tetanic stimulation; amnesia mechanism; deep reinforcement learning multi-DOF manipulator; tetanic stimulation; amnesia mechanism; deep reinforcement learning
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MDPI and ACS Style

Hou, Y.; Hong, H.; Xu, D.; Zeng, Z.; Chen, Y.; Liu, Z. A Deep Reinforcement Learning Algorithm Based on Tetanic Stimulation and Amnesic Mechanisms for Continuous Control of Multi-DOF Manipulator. Actuators 2021, 10, 254. https://doi.org/10.3390/act10100254

AMA Style

Hou Y, Hong H, Xu D, Zeng Z, Chen Y, Liu Z. A Deep Reinforcement Learning Algorithm Based on Tetanic Stimulation and Amnesic Mechanisms for Continuous Control of Multi-DOF Manipulator. Actuators. 2021; 10(10):254. https://doi.org/10.3390/act10100254

Chicago/Turabian Style

Hou, Yangyang, Huajie Hong, Dasheng Xu, Zhe Zeng, Yaping Chen, and Zhaoyang Liu. 2021. "A Deep Reinforcement Learning Algorithm Based on Tetanic Stimulation and Amnesic Mechanisms for Continuous Control of Multi-DOF Manipulator" Actuators 10, no. 10: 254. https://doi.org/10.3390/act10100254

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