Title: ACtuAL: Actor-Critic Under Adversarial Learning,
Abstract: Generative Adversarial Networks (GANs) are a powerful framework for deep
generative modeling. Posed as a two-player minimax problem, GANs are typically
trained end-to-end on real-valued data and can be used to train a generator of
high-dimensional and realistic images. However, a major limitation of GANs is
that training relies on passing gradients from the discriminator through the
generator via back-propagation. This makes it fundamentally difficult to train
GANs with discrete data, as generation in this case typically involves a
non-differentiable function. These difficulties extend to the reinforcement
learning setting when the action space is composed of discrete decisions. We
address these issues by reframing the GAN framework so that the generator is no
longer trained using gradients through the discriminator, but is instead
trained using a learned critic in the actor-critic framework with a Temporal
Difference (TD) objective. This is a natural fit for sequence modeling and we
use it to achieve improvements on language modeling tasks over the standard
Teacher-Forcing methods.