Simulation and Optimization of Continuous Motion Control Based on Spiking Reinforcement Learning

doi:10.16182/j.issn1004731x.joss.24-0380

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.

Key words: spiking neural network, RL, automatic motion control, feature transformation

CLC Number:

TP242.6

Liu Xiaode, Guo Yufei, Chen Yuanpei, Zhou Jie, Zhang Yuhan, Peng Weihang, Ma Zhe. Simulation and Optimization of Continuous Motion Control Based on Spiking Reinforcement Learning[J]. Journal of System Simulation, 2025, 37(10): 2662-2671.

Figures/Tables 9

Fig. 1

Fig. 2

Table 1

Degrees of freedom and rules of reward and punishment for models

指标	Ant-v3	HalfCheetah-v3	Hopper-v3	HumanoidStandup-v2	Humanoid-v3	Walker2d-v3
运动自由度	8	6	3	17	17	6
控制目标	四足行走	快速奔跑	弹跳行走	站立	双足行走	双足行走
奖惩规则	$R f + R h - R c - R t$	$R f - R c$	$R f + R h - R c$	$R f + R h - R c - R t$	$R f + R h - R c - R t$	$R f + R h - R c$
$R f$ 默认值	1.0	1.0	1.0	0.25	0.25	1.0
$R h$ 默认值	1.0		1.0	5.0	5.0	1.0
$w c$ 默认值	0.5	0.1	0.001	0.1	0.1	0.001
$w t$ 默认值	0.0005			5×10^-7	5×10^-7

Table 1

Fig. 3

Table 2

Fig. 4

Fig. 5

Table 3

Fig. 6

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模型	算法	本文结果	基准算法结果	文献[4]结果
Ant-v3	DDPG	6 001±621	4 230±1 016	5 220±625
Ant-v3	TD3	5 796±422	4 593±1 109	5 220±625
HalfCheetah-v3	DDPG	11 473±552	8 577±927	10 505±636
HalfCheetah-v3	TD3	11 000±751	10 547±897	10 505±636
Hopper-v3	DDPG	2 195±335	1 989±684	3 289±292
Hopper-v3	TD3	2 635±527	1 839±708	3 289±292
HumanoidStandup-v2	DDPG	203 890±31 678	97 650±10 876
HumanoidStandup-v2	TD3	214 340±22 026	139 850±29 721
Humanoid-v3	DDPG	1 167±176	301±100
Humanoid-v3	TD3	1 961±363	1 109±135
Walker2d-v3	DDPG	4 921±255	2 005±677	4 280±987
Walker2d-v3	TD3	4 878±228	3 951±551	4 280±987

模型	TD3方法	最终位移/m	最终速度/(m/s)
Hopper-v3	本文方法	19.87	2.47
Hopper-v3	基准方法	10.90	1.75
Walker2d-v3	本文方法	29.13	3.50
Walker2d-v3	基准方法	27.78	2.89