Temporal difference learning is a family of model-free reinforcement learning methods that update value estimates by using other current estimates as partial targets. It combines aspects of Monte Carlo sampling and dynamic programming, allowing agents to learn from experience before an episode has fully completed.
A class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods, and perform updates based on current estimates, like dynamic programming methods.