基于神经网络增强学习算法的工艺任务分配方法

在任务分配问题中,如果Markov决策过程模型的状态-动作空间很大就会出现"维数灾难".针对这一问题,提出一种基于BP神经网络的增强学习策略.利用BP神经网络良好的泛化能力,存储和逼近增强学习中状态-动作对的Q值,设计了基于Q学习的最优行为选择策略和Q学习的BP神经网络模型与算法.将所提方法应用于工艺任务分配问题,经过Matlab软件仿真实验,结果证实了该方法具有良好的性能和行为逼近能力.该方法进一步提高了增强学习理论在任务分配问题中的应用价值.

Research on Task Allocation of Process Planning Based on Reinforcement Learning and Neural Network

Aiming at the curse of dimensionality caused by prodigiousness of state-action space for Markov decision-making process model,a kind of Q learning method based on neural network was proposed.The Q value of a state-action pair during reinforcement learning was approached and stored by means of the high generalizability of BP neural network,then the optimal strategy based on Q learning for selection of action and a BP neural network model and algorithm for Q learning were designed.The algorithm proposed was a...