GRASP on Robotics is an inaugural series of talks hosted by the GRASP Laboratory. GRASP leverages academic, research, and industry connections to deliver a set of high class tech talks with the mission of providing technical topics and meaningful discussions.
Join the live-stream here Fridays from 11:00am -11:50am followed by a Q&A panel between our speaker, faculty, and students until 12:15pm.
[VIRTUAL] GRASP On Robotics: Drew Bagnell, Aurora, “Learning and Games in Self-Driving”
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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.
We 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.