Temporal-Difference (TD) learning is a model-free reinforcement learning approach that updates value functions based on the difference between successive predictions. It learns directly from experience by bootstrapping, using estimated future values to improve current estimates, enabling online learning without an environment model.
Temporal-Difference learning is a key method in AI that helps computer programs learn how to make good decisions over time, even when they don't fully understand how their actions will affect the future. It works by constantly updating its predictions based on new experiences, making it smarter as it goes.
TD learning, TD methods, Temporal Difference
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