The TD learning algorithm was introduced by Sutton in 1988. The algorithm takes the benefits of both the Monte Carlo method and dynamic programming (DP) into account. Like the Monte Carlo method, it doesn't require model dynamics, and like DP it doesn't need to wait until the end of the episode to make an estimate of the value function. Instead, it approximates the current estimate based on the previously learned estimate, which is also called bootstrapping. If you see in Monte Carlo methods there is no bootstrapping, we made an estimate only at the end of the episode but in TD methods we can bootstrap.