Animation simulation method of AR augmented reality large screen interaction based on deep reinforcement learning

The invention discloses an AR augmented reality large-screen interactive animation simulation method based on depth reinforcement learning, which collects action data of professionals and divides thedata as a reference action set. Firstly, two caffe convolution neural network frameworks are constructed, in which the states, actions and targets of animation characters are used as the first input,and the states, actions, targets and actions of lower limbs with wide range of motion are used as the inputs of the second grid, which is used to assist each other with the first network to acceleratethe learning rate. Animated characters are driven by PD controller, embedded in AR augmented reality system directly, or combined with the original animated characters in AR augmented reality system.The invention constructs a virtual animation character, through the reward and punishment information fed back to the character, makes the character know whether its behavior is correct or not, and through long-time study, the