基于遗传算法和神经网络预测的再励学习

提出一种基于遗传算法和神经网络预测法相结合的再励学习方法，利用遗传算法对全局进行最优解搜索，将进化过程中产生的数据用来训练神经网络预测器，当再励学习逼近最优解时，利用预测网络估计动作网络的参数、结构与系统响应之间的映射关系，用预测网络逼近最优解的能力引导遗传算法在局部向最优解快速逼近，以解决遗传算法局部振荡问题，从而实现快速学习的能力。将其应用于矢量控制交流电机的速度环控制器自学习中，仿真实验验证了该算法的有效性。

Reinforcement Learning Based on Genetic Algorithms and Neural Network Prediction

This paper proposes a self-learning algorithms of neural networks based on Genetic Algorithms(GA) and neural network prediction.It uses GA to search the optimum resolution globally.The data generated during the evolutionary process is used to train the predictive networks.When the self-learning of networks converges on the adjacent domains of optimum resolution,it uses the prediction network to predict the map between network parameters and system response.The neural networks' optimum resolution approximate...