{"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/the-assistive-multi-armed-bandit","title":"The Assistive Multi-Armed Bandit","arxiv_id":"1901.08654","date":"2019-01-24","proceeding":null,"authors":["Lawrence Chan","Dylan Hadfield-Menell","Siddhartha Srinivasa","Anca Dragan"],"abstract":"Learning preferences implicit in the choices humans make is a well studied\nproblem in both economics and computer science. However, most work makes the\nassumption that humans are acting (noisily) optimally with respect to their\npreferences. Such approaches can fail when people are themselves learning about\nwhat they want. In this work, we introduce the assistive multi-armed bandit,\nwhere a robot assists a human playing a bandit task to maximize cumulative\nreward. In this problem, the human does not know the reward function but can\nlearn it through the rewards received from arm pulls; the robot only observes\nwhich arms the human pulls but not the reward associated with each pull. We\noffer sufficient and necessary conditions for successfully assisting the human\nin this framework. Surprisingly, better human performance in isolation does not\nnecessarily lead to better performance when assisted by the robot: a human\npolicy can do better by effectively communicating its observed rewards to the\nrobot. We conduct proof-of-concept experiments that support these results. We\nsee this work as contributing towards a theory behind algorithms for\nhuman-robot interaction.","url_abs":"http://arxiv.org/abs/1901.08654v1","url_pdf":"http://arxiv.org/pdf/1901.08654v1.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":"the-assistive-multi-armed-bandit","repo_url":"https://github.com/chanlaw/assistive-bandits","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"multi-armed-bandits","task_name":"Multi-Armed Bandits"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1901.08654","atlas_url":"https://app.syntology.ai/?focus=1901.08654","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}