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Tea Time Talk: Ajin Joseph

  • University of Alberta 3-33 Computing Science Centre Edmonton, AB Canada (map)

Topic: Description:Model based search methods and their application to RL

The objective of the talk is to introduce a particular class of zero-order optimization methods called model-based search methods which are gradient-free techniques to generate high quality solutions to the optimization (deterministic or stochastic) problem. These methods have the unique characteristic to operate in a black-box setting where it is presumed that the analytic closed form expression of the objective function is unavailable and hence they are completely non-dependent on the structural properties of the objective function. In this talk, I explore various algorithms of this class, their efficient extensions and their application to reinforcement learning.


The Tea Time Talks are a series of talks primarily given by the students and faculty studying Artificial Intelligence at the University of Alberta, and provide a comfortable, informal space in which to listen and learn about topics pertaining to machine intelligence and machine learning.

Earlier Event: June 20
Tea Time Talk: Chen Ma
Later Event: June 22
AI Seminar: Levi Lelis