SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

TLDR

Generative Adversarial Networks have succeeded at producing real‑valued data, yet their use for discrete token sequences is limited by non‑differentiable outputs and a discriminator that can only evaluate complete sequences. This paper introduces SeqGAN, a sequence‑generation framework designed to overcome these limitations. SeqGAN models the generator as a stochastic policy in reinforcement learning, updating it with policy gradients driven by discriminator‑derived rewards that are propagated to intermediate steps via Monte Carlo search. Experiments on synthetic and real‑world datasets demonstrate that SeqGAN achieves markedly better performance than strong baseline methods.

Abstract

As a new way of training generative models, Generative Adversarial Net (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major reason lies in that the discrete outputs from the generative model make it difficult to pass the gradient update from the discriminative model to the generative model. Also, the discriminative model can only assess a complete sequence, while for a partially generated sequence, it is non-trivial to balance its current score and the future one once the entire sequence has been generated. In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems. Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines.