  One of the challenging problems in sequence generation tasks is the optimized
generation of sequences with specific desired goals. Current sequential
generative models mainly generate sequences to closely mimic the training data,
without direct optimization of desired goals or properties specific to the
task. We introduce OptiGAN, a generative model that incorporates both
Generative Adversarial Networks (GAN) and Reinforcement Learning (RL) to
optimize desired goal scores using policy gradients. We apply our model to text
and real-valued sequence generation, where our model is able to achieve higher
desired scores out-performing GAN and RL baselines, while not sacrificing
output sample diversity.
