time difference learning

Time difference learning is a type of reinforcement learning method in which an agent learns from the differences between predicted and actual outcomes over time. This approach is based on the idea that the agent can update its predictions based on the temporal difference between the expected and actual rewards it receives.

The time difference learning algorithm is commonly used in the context of Markov decision processes (MDPs) and is particularly useful for problems with delayed rewards. In these cases, the agent must learn to balance immediate rewards with long-term goals, which can be challenging without a mechanism for temporal difference learning.

Overall, time difference learning is a powerful tool for developing reinforcement learning algorithms that can learn from experience over time and make informed decisions based on past outcomes.