Deterministic Generative Adversarial Imitation Learning

Abstract This paper proposes a deterministic generative adversarial imitation learning method which allows the robot to implement the motion planning task rapidly by learning from the demonstration data without reward function. In our method, the deep deterministic policy gradient method is used as the generator for learning the action policy on the basis of discriminator, and the demonstration data is input into the generator to ensure its stability. Three experiments on the push and pick-and-place tasks are conducted in the gym robotic environment. Results show that the learning speed of our method is much faster than the stochastic generative adversarial imitation learning method, and it can effectively learn from the demonstration data in different states of the task with higher learning stability. The proposed method can complete the motion planning task without environmental reward quickly and improve the stability of the training process.