Temporal Difference

Temporal difference (TD) learning is a core reinforcement learning method aiming to estimate the value of states or state-action pairs by bootstrapping from predictions of future values. Current research focuses on improving TD's efficiency and stability, particularly when combined with deep neural networks, by addressing issues like variance reduction, handling uncertainty, and optimizing algorithm parameters (e.g., step size, target network updates). These advancements are significant for enhancing the performance and robustness of reinforcement learning agents across various applications, from robotics and game playing to more complex control problems and even supervised learning tasks.