SeqGAN-Poem-torch-PARL

using PARL reinforement learning framework with torch to implement SeqGAN(Chinese Poem generation)

original paper: SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

requirements.txt:

https://github.com/AddASecond/SeqGAN-Poem-torch-PARL/blob/master/SeqGAN-Poem-torch-PARL/requirements.txt

Introduction

这是一个用SeqGAN生成中文古诗的程序，使用百度的强化学习PARL框架以及pytorch。动态图如下（可以看到刚开始生成的序列结尾不太好，有很多“一”，后续逐渐变好）：

This is a project that using SeqGAN to generate Chinese Poem, where baidu's reinforcement learning framework PARL(with pytorch) are used.

github link of baidu's reinforcement learning framework PARL: https://github.com/PaddlePaddle/PARL

Arichitecture

This is how PARL abstracts RL as model-algorithm-agent:

And this is how SeqGAN works:

This is how I put SeqGAN into PARL framework:

• generator is actor/agent, generator.step gives "actions"(how to choose word), generator.sample (MTCS search in SeqGAN) gives the "states"(whole sequence samples) each episode(here one episode ends means the whole sequence are generated)

• discriminator and rollout are critic/environments, which obtain samples/embedding, output rewards

• rewards(loss) are used to train critic/env(discriminator) and actor/agent(generator)

• All PARL-related codes are used in train_generator_PG in main function

TODOs

• train using Poems as corpus *-Done
• using PARL framework *-Done
• using build-in functions in PARL to substitude some function *-ing
• increasing training stability *-ing (gen loss in experiment-log are not used, ignore it)

Thanks

most of code borrow from https://github.com/X-czh/SeqGAN-PyTorch and https://github.com/TobiasLee/SeqGAN_Poem, but merge them into PARL framework for better understanding of the RL process in SeqGAN.