{"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-with-a","title":"Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads","arxiv_id":"1606.03667","date":"2016-06-12","proceeding":"EMNLP 2016 11","authors":["Ji He","Mari Ostendorf","Xiaodong He","Jianshu Chen","Jianfeng Gao","Lihong Li","Li Deng"],"abstract":"We introduce an online popularity prediction and tracking task as a benchmark\ntask for reinforcement learning with a combinatorial, natural language action\nspace. A specified number of discussion threads predicted to be popular are\nrecommended, chosen from a fixed window of recent comments to track. Novel deep\nreinforcement learning architectures are studied for effective modeling of the\nvalue function associated with actions comprised of interdependent sub-actions.\nThe proposed model, which represents dependence between sub-actions through a\nbi-directional LSTM, gives the best performance across different experimental\nconfigurations and domains, and it also generalizes well with varying numbers\nof recommendation requests.","url_abs":"http://arxiv.org/abs/1606.03667v4","url_pdf":"http://arxiv.org/pdf/1606.03667v4.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-with-a","repo_url":"https://github.com/jvking/reddit-RL-simulator","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"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":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.03667","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}