论文标题
使用峰值神经网络进行奖励调整学习，以进行车辆横向控制
Reward-modulated learning using spiking neural networks for vehicle lateral control
论文作者
论文摘要
本文介绍了一个基于尖峰神经网络的车辆横向控制器，能够复制基于模型的控制器的行为，但具有额外的在线适应能力。通过利用神经可塑性并得益于奖励调制学习，对神经连接进行了修改以根据所承诺的错误调整响应。因此，该误差在生物系统中发挥与多巴胺相似的作用，从而根据峰值时间依赖性调节学习过程。连接最初设置为复制基于模型的控制器的行为。在线适应允许调整连接参数可以提高控制器性能。带有预览的路径控制器用作基线控制器，以评估所提出的方法的性能。通过带有步骤响应的模拟和具有20个不同半径曲线的处理轨道获得的关键性能指标。
This paper presents a vehicle lateral controller based on spiking neural networks capable of replicating the behavior of a model-based controller but with the additional ability to perform online adaptation. By making use of neural plasticity and thanks to reward modulation learning, neural connections are modified to adjust the response according to the committed error. Therefore, the error performs a similar role to dopamine in a biological system, modulating the learning process based on spiking time dependency. The connections are initially set to replicate behavior of the model-based controller. Online adaptation allows tuning connection parameters to improve controller performance. A path controller with a preview is used as a baseline controller to evaluate the performance of the proposed approach. Key performance indicators are obtained from simulation with a step response and a handling track with 20 curves of different radii.
