UCSB COMPUTER SCIENCE DEPARTMENT PRESENTS FACULTY CANDIDATE:
SOUMYA RAY Oregon State University
DATE: MONDAY, MARCH 17, 2008
TIME: 3:30 – 4:30 p.m.
PLACE: Computer Science Conference Room, Harold Frank Hall Rm. 1132
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.
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.
HOST: John Gilbert