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.

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