{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T15:34:08Z","timestamp":1787499248528,"version":"build-2736575974"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>We study the problem of inverse reinforcement learning (IRL) with the added twist that the learner is assisted by a helpful teacher. More formally, we tackle the following algorithmic question: How could a teacher provide an informative sequence of demonstrations to an IRL learner to speed up the learning process? We present an interactive teaching framework where a teacher adaptively chooses the next demonstration based on learner's current policy. In particular, we design teaching algorithms for two concrete settings: an omniscient setting where a teacher has full knowledge about the learner's dynamics and a blackbox setting where the teacher has minimal knowledge. Then, we study a sequential variant of the popular MCE-IRL learner and prove convergence guarantees of our teaching algorithm in the omniscient setting. Extensive experiments with a car driving simulator environment show that the learning progress can be speeded up drastically as compared to an uninformative teacher.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/374","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"2692-2700","source":"Crossref","is-referenced-by-count":12,"title":["Interactive Teaching Algorithms for Inverse Reinforcement Learning"],"prefix":"10.24963","author":[{"given":"Parameswaran","family":"Kamalaruban","sequence":"first","affiliation":[{"name":"LIONS, EPFL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rati","family":"Devidze","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Software Systems (MPI-SWS)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Volkan","family":"Cevher","sequence":"additional","affiliation":[{"name":"LIONS, EPFL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adish","family":"Singla","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Software Systems (MPI-SWS)"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:48:52Z","timestamp":1564285732000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/374"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/374","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}