Monday January 14 4:00 - 4:50 PM Kelley 1001
Soumya Ray Postdoctoral Researcher School of Electrical Engineering & Computer Science Oregon State University Knowledge Transfer in Reinforcement Learning Humans are remarkably good at using knowledge acquired while solving past problems to efficiently solve novel, related problems. How can we build 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 two approaches that allow agents to leverage experience gained from solving prior RL tasks. In the first approach, the agent learns a hierarchical Bayesian model from previously solved RL tasks that it uses to quickly infer the parameters of a novel RL task. In the second approach, the agent learns a hierarchical task-subtask decomposition from the solution of a previous task, and uses the decomposition to learn more quickly on a novel task. I present empirical evidence on two RL problem domains, maze-navigation and a resource collection problem in the real-time strategy game, Stratagus, that show that leveraging experience from prior RL tasks improves the rate of convergence to a solution in a new task. This is joint work with Aaron Wilson, Neville Mehta, Alan Fern, Prasad Tadepalli and Tom Dietterich at Oregon State University under DARPA grant FA8750-05-2-0249. Biography 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 bioinformatics.
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