{"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/deep-reinforcement-learning-for-multi-domain","title":"Deep Reinforcement Learning for Multi-Domain Dialogue Systems","arxiv_id":"1611.08675","date":"2016-11-26","proceeding":null,"authors":["Heriberto Cuayáhuitl","Seunghak Yu","Ashley Williamson","Jacob Carse"],"abstract":"Standard deep reinforcement learning methods such as Deep Q-Networks (DQN)\nfor multiple tasks (domains) face scalability problems. We propose a method for\nmulti-domain dialogue policy learning---termed NDQN, and apply it to an\ninformation-seeking spoken dialogue system in the domains of restaurants and\nhotels. Experimental results comparing DQN (baseline) versus NDQN (proposed)\nusing simulations report that our proposed method exhibits better scalability\nand is promising for optimising the behaviour of multi-domain dialogue systems.","url_abs":"http://arxiv.org/abs/1611.08675v1","url_pdf":"http://arxiv.org/pdf/1611.08675v1.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":"deep-reinforcement-learning-for-multi-domain","repo_url":"https://github.com/cuayahuitl/SimpleDS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"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"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dqn","method_name":"DQN"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"q-learning","method_name":"Q-Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.08675","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}