TD learning converges to the optimal condition as long as each action of every state has a probability of greater than zero of being chosen. To satisfy this requirement, TD methods, as we saw in the previous section, have to explore the environment. Indeed, the exploration can be carried out using an -greedy policy. It makes sure that both greedy actions and random actions are chosen in order to ensure both the exploitation and exploration of the environment.