Seminar 5 : Time, bounded rationality and representations in reinforcement learning

Date: February 18 (next Thursday)
Time: 1:00-2:00pm
Location: McConnell Room 320
 
Speaker: Pierre-Luc Bacon
Title: Time, bounded rationality and representations in reinforcement learning
 

Abstract:
In this talk, I will tell about some of the research that I’ve been conducting with Prof. Doina Precup over the last couple years.  The main topic will be the problem of “temporal representation learning” in reinforcement learning. Reinforcement learning is an Artificial Intelligence approach to the problem of sequential decision making, in a world full of uncertainty and under limited computational capacity. As for “representation learning”,  it refers to the problem of autonomously finding and  expressing knowledge within a particular reasoning structure while also improving it over time through experience. I will develop  these ideas through the notion of “bounded rationality” and present some recent mathematical tools that we developed to tackle  this problem. In a sense, the title of this talk also reflects my experience through PhD: a journey to improve and refine my own subjective understanding of the world (and of myself) under limited capacity. Just as for our reinforcement learning agents, I had to  embrace the stochasticity of life while leveraging its regularities. I will try to share both sides of the story: how the research results came about, and how I became more of a researcher over time.

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