# OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation

arXiv: https://arxiv.org/abs/2004.07534 · HTML: https://mlsanity.com/paper/2004.07534/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Game of GANs: Game-Theoretical Models for Generative Adversarial Networks](https://mlsanity.com/paper/2106.06976/) (2021)
- [Multi-Agent Simulation for AI Behaviour Discovery in Operations Research](https://mlsanity.com/paper/2108.13296/) (2021)

## Abstract (arXiv)

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.