Reinforcement Learning

Temporal Difference (TD) Learning is a fundamental concept in the field of reinforcement learning, which is a subfield of artificial intelligence (AI). It is particularly powerful for problems where an agent must learn to make decisions over time based on its interactions with an environment. Unlike traditional supervised learning, where a model learns from a fixed dataset, TD Learning enables agents to learn directly from experience, making it well-suited for dynamic and uncertain environments.