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Graph."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:p>In this paper we show how the task of motion tracking for physically simulated characters can be solved using supervised learning and optimizing a policy directly via back-propagation. To achieve this we make use of a world model trained to approximate a specific subset of the environment's transition function, effectively acting as a differentiable physics simulator through which the policy can be optimized to minimize the tracking error. Compared to popular model-free methods of physically simulated character control which primarily make use of Proximal Policy Optimization (PPO) we find direct optimization of the policy via our approach consistently achieves a higher quality of control in a shorter training time, with a reduced sensitivity to the rate of experience gathering, dataset size, and distribution.<\/jats:p>","DOI":"10.1145\/3478513.3480527","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T18:29:20Z","timestamp":1639160960000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":60,"title":["SuperTrack"],"prefix":"10.1145","volume":"40","author":[{"given":"Levi","family":"Fussell","sequence":"first","affiliation":[{"name":"Ubisoft La Forge and The University of Edinburgh, UK"}]},{"given":"Kevin","family":"Bergamin","sequence":"additional","affiliation":[{"name":"Ubisoft La Forge, Ubisoft, Canada"}]},{"given":"Daniel","family":"Holden","sequence":"additional","affiliation":[{"name":"Ubisoft La Forge, Ubisoft, Canada"}]}],"member":"320","published-online":{"date-parts":[[2021,12,10]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research","author":"Bengio Emmanuel","year":"2020","unstructured":"Emmanuel Bengio , Joelle Pineau , and Doina Precup . 2020 . 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