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[Colloquium] Knowledge Transfer in Reinforcement Learning

February 19, 2008

Watch Colloquium:

  • Date: Tuesday, February 19, 2008 
  • Time: 11 am — 12:15 pm 
  • Place: ME 218

Soumya Ray
Postdoctoral Researcher
Oregon State University

Abstract: Humans are remarkably good at using knowledge acquired while solving past problems to efficiently solve novel, related problems. How can we build artificial agents with similar capabilities? In this talk, I focus on “reinforcement learning” (RL)—a setting where an agent must make a sequence of decisions to reach a goal, with intermittent feedback from the environment about the cost of its current decision. I describe an approach that allows agents to leverage experience gained from solving prior RL tasks. To do this, the agent learns a hierarchical Bayesian model from previously solved RL tasks and uses it to quickly infer the characteristics of a novel RL task. I present empirical evidence on navigation problems and tactical battle scenarios in a real-time strategy game, Wargus, that show that leveraging experience from prior tasks improves the rate of convergence to a solution in a new task.

Bio: Soumya Ray obtained his baccalaureate degree from the Indian Institute of Technology, Kharagpur, and his doctorate from the University of Wisconsin, Madison in 2005. Since 2006, he has been a postdoctoral researcher in the machine learning group at Oregon State University. His research interests are in statistical machine learning, reinforcement learning and planning, and bioinformatics.