AI Seminar Series 2023: Kenny Young

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 24, Kenny Young — PhD student at the University of Alberta — presented “The Benefits of Model-Based Generalization in Reinforcement Learning" at the AI Seminar.

Abstract:

It is widely believed that model-based reinforcement learning can improve sample efficiency by synthesizing imagined experience which generalizes beyond the data. However, learned value functions also generalize. Why should we expect model generalization to be inherently better? In this talk, Young addresses this question with a simple theoretical result. He also presents extensive empirical results which demonstrate that the intuition behind the theory leads to significant practical benefits in environments with underlying structure that allows learned models to generalize.

Watch the full presentation below:

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