Temporal Difference Learning

Temporal Difference Learning (TD Learning) is a method used in machine learning, especially in reinforcement learning, to train algorithms to make decisions over time. It combines ideas from dynamic programming and Monte Carlo methods. Instead of waiting until the end of a task to update knowledge, TD Learning updates predictions based on new experiences at each step. This allows the system to learn from mistakes during the task itself, improving its decision-making in real-time. It's like learning to play a game by adjusting strategies as you play rather than reflecting only after the game is over.