AI Seminar Series 2020: Emilie Kaufmann on learning good policies with and without rewards

The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.

On November 20, 2020, CNRS researcher in the CRIStAL laboratory at the University of Lille (France) Emilie Kaufmann presented “On the complexity of learning good policies with and without rewards”.

The talk focused on two performance criteria that have been studied in the context of episodic reinforcement learning: Best Policy Identification (BPI) and Reward Free Exploration. Emilie explores their uses, alternative algorithms and relative complexity.

Watch the full presentation below:

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