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DTSTART;TZID=America/New_York:20210416T110000
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UID:4793-1618570800-1618576200@seasevents.nmsdev7.com
SUMMARY:GRASP On Robotics: “Learning and Games in Self-Driving”
DESCRIPTION:Abstract: In this talk\, I’ll explore the power of a game-theoretic viewpoint in self-driving and in machine learning. We begin by considering the application of machine learning to Aurora’s advanced self-driving system in both perception and decision making. We discuss complexities that arise from multi-actor interaction. \nWe then explore the\, perhaps surprising\, role a game-theoretic view can take in developing algorithms for learning to make decisions. In particular\, we review a “no-regret” game-theoretic perspective on model-based RL\, Approximate Policy Iteration\, and Inverse Optimal Control. \nClick here to join the Zoom Webinar
URL:https://seasevents.nmsdev7.com/event/grasp-on-robotics-learning-and-games-in-self-driving/
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