An injection production regulation model based on improved deep reinforcement learning is proposed. Firstly,es-tablish an injection and mining regulation and strengthening environment with maximizing economic benefits as the objective function. Secondly,aiming at the problems of large number of model parameters and many covariate displacements within the network,a deep reinforcement learning method to improve the double deep Q network is proposed,and the input data of the model is normalized layer by layer by applying batch normalization technology to enhance the generalization ability of the model,and then the model volume is compressed through the pruning module to accelerate the training process of the network,and the dynamic ε strategy idea is introduced to improve the robustness and stability of the model. Finally,the proposed model is compared with other models,and the experimental results show that the proposed model can obtain higher and more stable average cumulative reward,faster convergence speed and running speed.
关键词
注采调控/深度强化学习/剪枝/批量归一化/双重深度Q网络
Key words
injection-production control/deep reinforcement learning/pruning/batch normalization/double deep Q network
维普
万方数据