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However, a key limitation of such techniques is that they require a priori knowledge of all players\u2019 objectives. In this work, we address this issue by proposing a novel method for learning players\u2019 objectives in continuous dynamic games from noise-corrupted, partial state observations. Our approach learns objectives by coupling the estimation of unknown cost parameters of each player with inference of unobserved states and inputs through Nash equilibrium constraints. By coupling past state estimates with future state predictions, our approach is amenable to simultaneous online learning and prediction in receding horizon fashion. We demonstrate our method in several simulated traffic scenarios in which we recover players\u2019 preferences, for, e.g. desired travel speed and collision-avoidance behavior. Results show that our method reliably estimates game-theoretic models from noise-corrupted data that closely matches ground-truth objectives, consistently outperforming state-of-the-art approaches.<\/jats:p>","DOI":"10.1177\/02783649231182453","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T02:21:35Z","timestamp":1687141295000},"page":"917-937","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["Online and offline learning of player objectives from partial observations in dynamic games"],"prefix":"10.1177","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9008-7127","authenticated-orcid":false,"given":"Lasse","family":"Peters","sequence":"first","affiliation":[{"name":"Department of Cognitive Robotic, Delft University of Technology, Delft, Netherlands"},{"name":"Department for Photogrammetry, University of Bonn, Bonn, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vicen\u00e7","family":"Rubies-Royo","sequence":"additional","affiliation":[{"name":"University of California Berkeley, Berkeley, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claire J","family":"Tomlin","sequence":"additional","affiliation":[{"name":"University of California Berkeley, Berkeley, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Ferranti","sequence":"additional","affiliation":[{"name":"Department of Cognitive Robotic, Delft University of Technology, Delft, Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javier","family":"Alonso-Mora","sequence":"additional","affiliation":[{"name":"Department of Cognitive Robotic, Delft University of Technology, Delft, 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