Temporal Difference Learning
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Temporal Difference Learning (TDL) is a powerful technique in the field of Reinforcement Learning. Reinforcement Learning deals with learning to make decisions in an environment to maximize some notion of long-term reward. It's used in a variety of real-world applications, such as training robots to walk, teaching self-driving cars how to navigate the world, and developing trading strategies for financial markets. As such, there are several career opportunities associated with reinforcement learning across a variety of industries.
What is Temporal Difference Learning?
TDL is used to approximate a value function - a function that estimates the value of a state in terms of future rewards. This is achieved by repeatedly updating the value function based on the difference between the value of the current state and the value of the next state. TDL algorithms are often used in conjunction with other reinforcement learning techniques, such as Q-learning and SARSA, to improve learning efficiency and stability.
