Patent ID: 11914376
Assignee: WUHAN UNIVERSITY OF TECHNOLOGY
Field: Control (Instruments)
Classification: CPC G  B | IPC G

Claim 3:
4. The method according to claim 1, wherein the step S3 comprises:
training the decision-making neural network model based on a deep deterministic policy gradient (DDPG) algorithm, and introducing an Ornstein-Uhlenbeck (OU) process into DDPG in the training process to explore the environment, wherein when the decision-making neural network model makes a decision and outputs an action at=μ(st), an action produced by a random process is ano-brake=μ(st)+N, an average value of noise N is 0, at indicates the action output by a neural network, μ(st) indicates a neural network decision strategy, st indicates a state input into the neural network at time T, and ano-brake indicates an action generated by adding random noise to the action output by the neural network.