文章基本信息

标题：A Deep Reinforcement Learning Algorithm Based on Tetanic Stimulation and Amnesic Mechanisms for Continuous Control of Multi-DOF Manipulator
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作者：Yangyang Hou ; Huajie Hong ; Dasheng Xu 等
期刊名称：Actuators
电子版ISSN：2076-0825
出版年度：2021
卷号：10
期号：10
页码：254
DOI：10.3390/act10100254
语种：English
出版社：MDPI Publishing
摘要：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.