AI Seminar Series 2021: Csaba Szepesvári, Hardness of MDP planning with linear function approximation

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 February 19, Csaba Szepesvári -- Amii Fellow, Canada CIFAR AI Chair, the team-lead for the “Foundations” team at DeepMind and a Professor of Computing Science at the University of Alberta -- presented “Hardness of MDP planning with linear function approximation”.

Markov decision processes (MDPs) is a minimalist framework designed to capture the most important aspects of decision-making under uncertainty. The price of the minimalist approach is that MDPs lack structure; planning and learning in MDPs with combinatorial-sized state and action spaces is strongly intractable. In this talk, Szepesvári discusses some recent results concerned with these computations.

Watch the full presentation below:

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