Temporal Difference (TD) learning is the central and novel theme of reinforcement learning. TD learning is the combination of both Monte Carlo (MC) and Dynamic Programming (DP) ideas. Like Monte Carlo methods, TD methods can learn directly from the experiences without the model of the environment. Similar to Dynamic Programming, TD methods update estimates based in part on other learned estimates, without waiting for a final outcome, unlike MC methods, in which estimates are updated after reaching the final outcome only.