Data: DS positive set P , DS negative set N G for generator G, DS negative set N D for discriminator D Input: Pre-trained G with parameters θ G on dataset (P , N G ); Pre-trained D with parameters θ D on dataset (P , N D ) Output: Adversarially trained generator G 1: Load parameters θ G for G 2: Split P into the bag sequence Load parameters θ D for D 5: Compute the probability Obtain the generated part T by sampling according to {p G (s j )} j=1...|B| and the rest set Calculate the reward r 12: 13: end for Compute the accuracy Optimizing Generator The objective of the generator is similar to the objective of the one-step reinforcement learning problem: Maximizing the expectation of a given function of samples from a parametrized probability distribution.
we use a policy gradient strategy to update the generator.