Promisingly, he showed that Q-learning would always “converge,” namely, as long as the system had the opportunity to try every action, from every state, as many times as necessary, it would always, eventually develop the perfect value function:

The DQN system used epsilon-greedy exploration, which involves learning about which actions produce reward by simply hitting buttons at random a certain fraction of the time.