Tensor Processing Units (TPUs)
Machine Learning
A learning method that combines ideas from Monte Carlo and dynamic programming to update estimates based on other estimates.
Detailed Explanation
Temporal Difference Learning is a machine learning technique that blends Monte Carlo methods and dynamic programming. It updates value estimates based on the difference between predicted and actual rewards over time, enabling agents to learn from incomplete episodes. This method allows for efficient, online learning and is fundamental in reinforcement learning, particularly for predicting future rewards in sequential decision-making tasks.
Use Cases
- •Use case: An autonomous robot improves navigation by updating its path estimates based on real-time reward predictions during exploration.
Related Terms
Other terms in the Machine Learning category