{"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/optiongan-learning-joint-reward-policy","title":"OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning","arxiv_id":"1709.06683","date":"2017-09-20","proceeding":null,"authors":["Peter Henderson","Wei-Di Chang","Pierre-Luc Bacon","David Meger","Joelle Pineau","Doina Precup"],"abstract":"Reinforcement learning has shown promise in learning policies that can solve\ncomplex problems. However, manually specifying a good reward function can be\ndifficult, especially for intricate tasks. Inverse reinforcement learning\noffers a useful paradigm to learn the underlying reward function directly from\nexpert demonstrations. Yet in reality, the corpus of demonstrations may contain\ntrajectories arising from a diverse set of underlying reward functions rather\nthan a single one. Thus, in inverse reinforcement learning, it is useful to\nconsider such a decomposition. The options framework in reinforcement learning\nis specifically designed to decompose policies in a similar light. We therefore\nextend the options framework and propose a method to simultaneously recover\nreward options in addition to policy options. We leverage adversarial methods\nto learn joint reward-policy options using only observed expert states. We show\nthat this approach works well in both simple and complex continuous control\ntasks and shows significant performance increases in one-shot transfer\nlearning.","url_abs":"http://arxiv.org/abs/1709.06683v2","url_pdf":"http://arxiv.org/pdf/1709.06683v2.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":"optiongan-learning-joint-reward-policy","repo_url":"https://github.com/Breakend/OptionGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"continuous-control","task_name":"Continuous Control"},{"task_slug":"imitation-learning","task_name":"Imitation Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"continuous-control","task_name":"continuous-control"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1709.06683","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}