ACM A.M. Turing Award for Andrew Barto and Richard Sutton (AI column)

ACM A.M. Turing Award received This year, Andrew Barto and Richard Sutton. This award is often referred to as the “Nobel Prize in Computer Science” and was given to them for their pioneering contributions to reinforcement learning. (reinforcement). Their cooperation, which began in 1980He introduced key concepts, mathematical foundations and influential algorithms that have become fundamental to modern AI systems. Reinforcement learning allows machines to learn optimal behavior by interacting with the environment through trial and error, guided by rewards and penalties.

Among their most significant achievements: Development of the concept temporal-difference learningTD-Lambda allows systems to learn from the difference between successive predictions. Mathematical foundations of reinforcement learning using Markov decision-making processes

• Development of policy gradient methods (policy-gradient methods) The concept of time abstraction, allowing AI to learn in stages, which is critical for systems that need to reason over long time horizons Using neural networks to represent the functions studied

The cornerstone of their legacy is the influential textbook.Reinforcement Learning: An Introduction“, first published in 1998 This year, with a significantly expanded second edition published in 2018 year. This book is widely recognized as a defining guide to reinforcement learning, clearly laying out key ideas and algorithms. By the way, I ordered a second edition of the book in English, by the end of March it will come to me and then I will try to read it:)

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