Roger Sutton
Irvine, California,United States
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Roger Sutton is a distinguished Canadian computer scientist, widely recognized as one of the founding fathers of modern reinforcement learning (RL). He is a Professor of Computing Science at the University of Alberta and a Distinguished Research Scientist at DeepMind. Sutton's research primarily focuses on learning problems where an agent must learn to make decisions through trial and error by interacting with its environment. He is best known for his significant contributions to temporal difference (TD) learning, policy gradient methods, and the development of the Dyna architecture. Together with Andrew Barto, he co-authored the seminal textbook 'Reinforcement Learning: An Introduction,' which has become a standard reference in the field and has educated a generation of AI researchers and practitioners. His work aims to understand and replicate the mechanisms of learning and intelligence found in nature.
Professional Summary
Roger Sutton's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Notable Achievements
Co-authored 'Reinforcement Learning: An Introduction'
Co-authored the highly influential and foundational textbook on reinforcement learning with Andrew G. Barto, which has served as the primary educational resource for students and researchers in the field for decades.
Pioneering Work on Temporal Difference (TD) Learning
Made seminal contributions to the theory and application of Temporal Difference (TD) learning, a core concept in reinforcement learning that combines ideas from dynamic programming and Monte Carlo methods for learning value functions.
Development of Policy Gradient Methods
Significantly advanced policy gradient methods, which allow agents to learn policies directly without necessarily learning a value function, crucial for tasks with continuous action spaces or complex policy representations.
Introduction of the Dyna Architecture
Proposed the Dyna architecture, an innovative framework that integrates learning from real experience with learning from simulated experience generated by a learned model of the environment, thereby improving sample efficiency.
Fellow of the Royal Society of Canada
Elected as a Fellow of the Royal Society of Canada, the country's national academy, in recognition of his outstanding scholarly and scientific achievements.
Fellow of the Association for the Advancement of Artificial Intelligence (AAAI)
Recognized as a Fellow of AAAI for his significant, sustained contributions to the field of artificial intelligence, particularly in reinforcement learning.
Educational Background
Masters Degree, Economics
California State University, Long Beach - Year 2011
Bachelors Degree, Mathematical Economics
California State University, Long Beach - Year 2001
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Company Overview
California Bank & TrustCalifornia Bank & Trust (CB&T) is a full-service bank providing a wide range of commercial, small business, and personal banking services. With branches throughout California, CB&T focuses on building relationships with its customers and offering tailored financial solutions. It emphasizes local decision-making and community involvement. California Bank & Trust is a division of Zions Bancorporation, N.A. (NASDAQ: ZION), one of the nation's premier financial services companies with operations in 11 Western states.
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