Temporal difference learning is a type of reinforcement learning that focuses on predicting future rewards based on current and past experiences. This method combines ideas from dynamic programming and Monte Carlo methods, allowing agents to learn from incomplete episodes. The learning process updates value estimates based on the difference between predicted and actual rewards over time, effectively reducing prediction errors and improving decision-making.
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