重复利用状态值的竞争深度Q网络算法

在使用反距离加权法(Inverse Distance Weighted method,IDW)对土壤重金属含量进行预测时,算法中的超参数一般由先验知识确定,一定程度上存在不确定性.针对这一问题,提出了一种状态值再利用的竞争深度Q学习网络算法以精确估计IDW的超参数.该算法在训练时,将每轮训练样本中的奖励值进行标准化后,...

Dueling Deep Q Network Algorithm with State Value Reuse

When using the Inverse Distance Weighted method（IDW） to predict the content of heavy metals in soil, the super parameters in the algorithm are generally determined by prior knowledge, and there is uncertainty to a certain extent. In order to solve this problem, a dueling deep Q-learning network algorithm for reusing state values is proposed to accurately estimate the hyper-parameters of IDW. In the training process, the reward value of each training sample is standardized and combined with the state value of Q network in Dueling-DQN to form a new total reward value, and then the total reward value is input into the Q network for learning, so as to enhance the internal relationship between state and action and make the algorithm more stable. Finally, this method is used to perform hyper-parameter search on the IDW, and compare experiments with several common deep learning algorithms. Experimental results show that the proposed RSV-DuDQN algorithm can make the model converge faster, improve the stability of the model, and get more accurate IDW parameter estimation.