Temporal difference learning is a reinforcement learning technique that combines elements of dynamic programming and Monte Carlo methods to update the value of states based on the difference between predicted and actual rewards over time. This approach enables an agent to learn and make decisions by estimating the value of future rewards without requiring a model of the environment. By focusing on the differences in predictions, temporal difference learning helps improve the accuracy of value estimates iteratively, making it especially useful in environments with delayed rewards.
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