Journal of System Simulation
Abstract
Abstract: To improve the model robustness for multi-degree-of-freedom continuous motion control, an intelligent motion control algorithm was proposed based on the Actor-Critic reinforcement learning framework and spiking neural networks. This algorithm integrateed the Actor network with spiking population coding and enhanced model training performance by introducing feature transformation methods. The Critic network was used to evaluate the effectiveness of the motion control. The results show that, compared to other reinforcement learning algorithms, the average reward value of this method increases by more than 10%. The simulation results validate the effectiveness of the model in improving multi-degree-of-freedom continuous control performance.
Recommended Citation
Liu, Xiaode; Guo, Yufei; Chen, Yuanpei; Zhou, Jie; Zhang, Yuhan; Peng, Weihang; and Ma, Zhe
(2025)
"Simulation and Optimization of Continuous Motion Control Based on Spiking Reinforcement Learning,"
Journal of System Simulation: Vol. 37:
Iss.
10, Article 19.
DOI: 10.16182/j.issn1004731x.joss.24-0380
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss10/19
First Page
2662
Last Page
2671
CLC
TP242.6; TP391.9
Recommended Citation
Liu Xiaode, Guo Yufei, Chen Yuanpei, et al. Simulation and Optimization of Continuous Motion Control Based on Spiking Reinforcement Learning[J]. Journal of System Simulation, 2025, 37(10): 2662-2671.
DOI
10.16182/j.issn1004731x.joss.24-0380
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