{"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/towards-solving-text-based-games-by-producing","title":"Towards Solving Text-based Games by Producing Adaptive Action Spaces","arxiv_id":"1812.00855","date":"2018-12-03","proceeding":null,"authors":["Ruo Yu Tao","Marc-Alexandre Côté","Xingdi Yuan","Layla El Asri"],"abstract":"To solve a text-based game, an agent needs to formulate valid text commands\nfor a given context and find the ones that lead to success. Recent attempts at\nsolving text-based games with deep reinforcement learning have focused on the\nlatter, i.e., learning to act optimally when valid actions are known in\nadvance. In this work, we propose to tackle the first task and train a model\nthat generates the set of all valid commands for a given context. We try three\ngenerative models on a dataset generated with Textworld. The best model can\ngenerate valid commands which were unseen at training and achieve high $F_1$\nscore on the test set.","url_abs":"http://arxiv.org/abs/1812.00855v1","url_pdf":"http://arxiv.org/pdf/1812.00855v1.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":"towards-solving-text-based-games-by-producing","repo_url":"https://github.com/taodav/TextWorldACG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"},{"task_slug":"text-based-games","task_name":"text-based games"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.00855","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}