Temporal Difference Learning

Temporal Difference Learning (TD Learning) is a reinforcement learning technique that combines ideas from dynamic programming and Monte Carlo methods. It allows an agent to learn how to predict future rewards based on its experiences, without requiring a model of the environment. TD Learning updates its value estimates based on the difference between predicted and actual rewards over time, hence the name. This approach is particularly useful in environments where the agent must make decisions sequentially and learn from delayed rewards. Common use cases include game playing, robotic control, and any scenario that involves learning from interaction with an environment.