LantaoYu/SeqGAN

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2092 stars · 695 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python/TensorFlow implementation of SeqGAN, the Sequence Generative Adversarial Nets with Policy Gradient model from the AAAI-17 paper. It trains a generator of discrete token sequences adversarially, using a discriminator as reward signal via Monte Carlo search and policy gradient.

Use cases

• reproduce the SeqGAN synthetic data experiments with oracle evaluation
• train a GAN to generate sequences of discrete tokens
• apply policy gradient adversarial training to text generation
• study reinforcement learning-based GAN training for sequences
• benchmark negative log-likelihood of sequence generators

When to choose

• you need a reference implementation of the SeqGAN paper for research or study
• you want to experiment with adversarial training of discrete sequence generators using policy gradient
• you are working with legacy TensorFlow r1.x and Python 2.7 environments

When to avoid

• you need a maintained library or modern framework support (requires TensorFlow r1.0.1 and Python 2.7)
• you want production text generation - modern transformer LLMs are far more effective
• you need a permissive license - the repository has no license, so reuse rights are unclear

Facets

library · maturity abandoned

machine-learning deep-learning machine-learning deep-learning python gan seqgan policy-gradient reinforcement-learning text-generation tensorflow research-code aaai-17 natural-language-processing linux gpu

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