Title: Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models,
Abstract: In unsupervised data generation tasks, besides the generation of a sample
based on previous observations, one would often like to give hints to the model
in order to bias the generation towards desirable metrics. We propose a method
that combines Generative Adversarial Networks (GANs) and reinforcement learning
(RL) in order to accomplish exactly that. While RL biases the data generation
process towards arbitrary metrics, the GAN component of the reward function
ensures that the model still remembers information learned from data. We build
upon previous results that incorporated GANs and RL in order to generate
sequence data and test this model in several settings for the generation of
molecules encoded as text sequences (SMILES) and in the context of music
generation, showing for each case that we can effectively bias the generation
process towards desired metrics.