The invention provides a
robot control method based on an off-
line model pre-training learning DDPG
algorithm, and the method comprises the following steps: collecting the training data of a 2D dummy in an off-line environment, and carrying out the preprocessing of the training data to obtain a training
data set; constructing and initializing an
artificial neural network and initializing parameters; pre-training the evaluation network and the action network offline by using the training
data set; initializing a target network by using the pre-trained evaluation network, and storing state conversion data into a storage buffer by the
intelligent agent to serve as an online
data set for training an online network; training an
online strategy network and an online Q network by using the online data set, and updating the
online strategy network and the online Q network by using a DDQN structure; carrying out soft updating, and controlling the state of the 2D dummy. According to the method, the efficiency is higher, the generated Q value is more accurate, the average
reward value is higher, the learning strategy is more stable and reliable, the convergence rate is increased, the obtained accumulated
reward value reaches a higher level, and the
robot can quickly arrive at the destination.