An important of both Monte Carlo TD methods is that they converge to an optimal solution as long as they deal with tabular cases (meaning that state values are stored in tables or arrays) and have an exploratory strategy. Nonetheless, they differ in the way they update the value function. Overall, TD learning has lower variance but suffers from a higher bias than Monte Carlo learning. In addition to this, TD methods are generally faster in practice and are preferred to Monte Carlo methods.