2020
DOI: 10.1007/978-3-030-66645-3_25
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Variable Impedance Control of Manipulator Based on DQN

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“…These reinforcement learning algorithms have good versatility and self-adaptability in the interaction process and perform well in the simulation environment, but when used in practical applications, they must often address multiple interactions. Therefore, some scholars have begun using the model-based method to reduce the number of actual interactions and improve the utilization rate of the algorithm (Hou et al, 2020 ). For example, Zhao et al ( 2022 ) proposed a model-based actor-critic learning algorithm to safely learn strategy and optimize the impedance control.…”
Section: Introductionmentioning
confidence: 99%
“…These reinforcement learning algorithms have good versatility and self-adaptability in the interaction process and perform well in the simulation environment, but when used in practical applications, they must often address multiple interactions. Therefore, some scholars have begun using the model-based method to reduce the number of actual interactions and improve the utilization rate of the algorithm (Hou et al, 2020 ). For example, Zhao et al ( 2022 ) proposed a model-based actor-critic learning algorithm to safely learn strategy and optimize the impedance control.…”
Section: Introductionmentioning
confidence: 99%