The process of making decisions is something humans do inherently and routinely, to the extent that it appears commonplace. However, in order to achieve good overall performance, decisions must take into account both the outcomes of past decisions and opportunities of future ones. Reinforcement learning, which is fundamental to sequential decisionmaking, consists of the following components: ① A set of decisions epochs; ② A set of environment states; ③ A set of available actions to transition states; ④ Stateaction dependent immediate rewards for each action.〓At each decision, the environment state provides the decision maker with a set of available actions from which to choose. As a result of selecting a particular action in the state, the environment generates an immediate reward for the decision maker and shifts to a different state and decision. The ultimate goal for the decision maker is to maximize the total reward after a sequence of time steps.〓This paper will focus on an archetypal example of reinforcement learning, the stochastic multiarmed bandit problem. After introducing the dilemma, I will briefly cover the most common methods used to solve it, namely the UCB and εn-greedy algorithms. I will also introduce my own greedy implementation, the strictgreedy algorithm, which more tightly follows the greedy pattern in algorithm design, and show that it runs comparably to the two accepted algorithms.