Temporal Difference Learning is a type of reinforcement learning where an agent learns to predict future rewards by comparing its current estimate with the subsequent reward it receives. This approach enables the agent to learn from incomplete episodes, adjusting its value estimates based on the difference between predicted and actual outcomes. It is closely related to concepts like bootstrapping and online learning, allowing for efficient updates of value functions.
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