This chapter lays the groundwork for understanding reinforcement learning (RL) by introducing key concepts and terminologies. We explore the Markov decision process (MDP) framework, which forms the backbone of many RL problems. Readers will gain insights into the components of an RL system, including states, actions, policies, and reward functions. We also discuss various RL formulations and their mathematical representations. By the end of this chapter, readers will have a solid foundation in RL concepts, preparing them for more advanced topics in subsequent chapters.