{"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/using-reinforcement-learning-to-learn-how-to","title":"Using reinforcement learning to learn how to play text-based games","arxiv_id":"1801.01999","date":"2018-01-06","proceeding":null,"authors":["Mikuláš Zelinka"],"abstract":"The ability to learn optimal control policies in systems where action space\nis defined by sentences in natural language would allow many interesting\nreal-world applications such as automatic optimisation of dialogue systems.\nText-based games with multiple endings and rewards are a promising platform for\nthis task, since their feedback allows us to employ reinforcement learning\ntechniques to jointly learn text representations and control policies. We\npresent a general text game playing agent, testing its generalisation and\ntransfer learning performance and showing its ability to play multiple games at\nonce. We also present pyfiction, an open-source library for universal access to\ndifferent text games that could, together with our agent that implements its\ninterface, serve as a baseline for future research.","url_abs":"http://arxiv.org/abs/1801.01999v1","url_pdf":"http://arxiv.org/pdf/1801.01999v1.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":"using-reinforcement-learning-to-learn-how-to","repo_url":"https://github.com/MikulasZelinka/pyfiction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"using-reinforcement-learning-to-learn-how-to","repo_url":"https://github.com/matthewsparr/Deep-Zork","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"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":"reinforcement-learning-2","task_name":"reinforcement-learning"},{"task_slug":"text-based-games","task_name":"text-based games"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}