Temporal Difference Learning is a method used in reinforcement learning that helps an agent learn how to make decisions over time. It combines ideas from both dynamic programming and Monte Carlo methods, allowing the agent to update its knowledge based on the difference between predicted and actual rewards. This approach enables the agent to learn from incomplete episodes, making it efficient in environments where it cannot always see the final outcome. In Temporal Difference Learning, the agent uses a value function to estimate how good a particular state is. As it interacts with the environment, it adjusts these estimates based on the rewards received, gradually improving its decision-making. This learning process helps the agent develop strategies that maximize its long-term rewards.