Tensorflow implementation of generative adversarial networks (GAN) applied to sequential data via recurrent neural networks (RNN).

See simple_demo.py for a demonstration of the model on toy data.

The basic idea of a generator and discriminator alternatively optimizing their own objectives is maintained. Because of discrete sequential data, a standard backpropogation from discriminator to generator is not possible. Rather, we employ the REINFORCE algorithm, to encourage the generator to choose the correct discrete output at each point in the sequence.

The REINFORCE algorithm is prone to issues with credit assignment. To alleviate this, the model provides 'supervised training' (as opposed to the 'unsupervised training' via the discriminator). During supervised training, the generator is trained to predict the correct tokens based on a groundtruth sequence, optimizing cross entropy loss.