Sutton-Barto Algorithm

The Sutton-Barto algorithm refers to a theory in reinforcement learning that focuses on how agents (like robots or software) learn from their experiences to make better decisions over time. It combines two key ideas: value functions, which estimate the quality of different actions based on past rewards, and temporal difference learning, which updates these estimates based on new experiences. Essentially, it helps machines learn to maximize rewards by predicting outcomes and adjusting their strategies accordingly, much like how humans learn from trial and error to improve their skills.