Sutton & Barto (Richard S. Sutton and Andrew G. Barto)

Sutton and Barto are researchers who developed foundational ideas in reinforcement learning, a type of machine learning where computers learn to make decisions through trial and error. They introduced the concept that systems can improve behavior by receiving feedback—rewards or penalties—from their environment. Their work explains how agents can learn optimal actions over time by predicting future rewards and updating their strategies accordingly. This framework helps machines and algorithms solve complex problems, like game playing or robotics, by mimicking how humans and animals learn from experience.