This chapter introduces some theoretical foundations of reinforcement learning and its applications to fluid mechanics from a control theory perspective. This choice is intended to provide the reader accustomed to flow control the key ideas in a more familiar vocabulary and a perspective on a vast literature blooming with tremendous momentum. First, we set the stage by introducing flow control and re-framing motivations and goals using reinforcement learning. Next, we shift our focus on some basic concepts by discussing the Hamilton-Jacobi-Bellman equation, the dynamic programming for nonlinear optimal control, and the iterative schemes used for approximate solution. Finally, the chapter closes by reconciling these elements with the terminology of reinforcement learning practice.