Why is Temporal-Difference (TD) learning considered significant in RL research?

Answer

It allows agents to learn directly from experience by updating value estimates based on successive predictions of future reward.

Temporal-Difference (TD) learning is highly significant because it enables agents to learn 'online' directly from experience, even when the environment’s final outcome is uncertain or far in the future. Instead of waiting until the termination of an episode to assess performance, TD methods update value estimates based on the difference between successive predictions of future reward. This ability to perform incremental updates based on prediction errors makes it crucial for complex, real-world problems where waiting for a definitive outcome is impractical or inefficient, thus providing a powerful mechanism for continuous learning.

Why is Temporal-Difference (TD) learning considered significant in RL research?

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