{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/objective-reinforced-generative-adversarial","title":"Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models","arxiv_id":"1705.10843","date":"2017-05-30","proceeding":null,"authors":["Gabriel Lima Guimaraes","Benjamin Sanchez-Lengeling","Carlos Outeiral","Pedro Luis Cunha Farias","Alán Aspuru-Guzik"],"abstract":"In unsupervised data generation tasks, besides the generation of a sample\nbased on previous observations, one would often like to give hints to the model\nin order to bias the generation towards desirable metrics. We propose a method\nthat combines Generative Adversarial Networks (GANs) and reinforcement learning\n(RL) in order to accomplish exactly that. While RL biases the data generation\nprocess towards arbitrary metrics, the GAN component of the reward function\nensures that the model still remembers information learned from data. We build\nupon previous results that incorporated GANs and RL in order to generate\nsequence data and test this model in several settings for the generation of\nmolecules encoded as text sequences (SMILES) and in the context of music\ngeneration, showing for each case that we can effectively bias the generation\nprocess towards desired metrics.","url_abs":"http://arxiv.org/abs/1705.10843v3","url_pdf":"http://arxiv.org/pdf/1705.10843v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"objective-reinforced-generative-adversarial","repo_url":"https://github.com/gablg1/ORGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"molecular-graph-generation","task_name":"Molecular Graph Generation"},{"task_slug":"music-generation","task_name":"Music Generation"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.10843","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}