条件变分自编码器生成潜在空间特征的模仿学习算法OA

The tasks in the high-dimensional environment are common in complex tasks.The characteristics of this type of task are the information data of the task environment and the control data of the robot containing many types,with high characteristic dimensions.The existing imitation learning method makes it difficult to learn better strategies because of the complicated distribution of teaching data by experts.In this paper,an imitation learning algorithm for generating latent space features using conditional variational autoencoders is designed to address the problem of long training time and limited application of imitation learning algorithms due to high-dimensional environment space and complex data distribution.By actively reducing the dimensionality of the environment space,the complexity of the neural network is reduced to speed up the training speed,and by utilizing the action loss prediction network and the perturbation layer,feedback is obtained from the output to improve the training accuracy.This paper verifies the effectiveness of the proposed algorithm through D4RL benchmark testing,simulated tests of continuous control tasks for the Microsoft MoCapAct humanoid robot,and complex manipulation tasks for the humanoid five-fingered robot.The results show that the method proposed in this paper has faster training speed,higher accuracy and more stable strategy.