In Sutton and Barto's reinforcement learning book, in multi-armed bandit problem a phrase has been used. "finding an optimal action" using greedy/$\epsilon$-greedy algorithm. When it is said that an algorithm "finds the optimal action " ?
In the bandit setting, to each arm is associated an unknown reward distribution. The optimization goal is to find a policy (a series of level pulls) which yields the maximum sum of rewards.
"Finding an optimal action" thus refers to the process of discovering the arm which gives you the most reward.