Temporal Difference Method

The Temporal Difference (TD) method is a way computers learn to make decisions step-by-step, by comparing their predictions about future rewards to actual outcomes. It updates its understanding gradually, using current experience to improve future guesses. For example, if a robot expects a certain reward after taking an action but receives a different result, it adjusts its strategy accordingly. This ongoing process allows the system to learn efficiently from ongoing interactions, improving its decision-making over time without needing complete knowledge from the start. It's a practical approach to learning from experience in dynamic environments.