This lecture covers the basic concepts in reinforcement learning. It starts from introducing the concepts such as agent and environment, Markov Decision Processes, value function, and policy. We study Dynamic Programming (DP)-based methods to finding the optimal value function and policy. The conventional DP, however, is not feasible when the state space is large or when the model of the MDP is unknown. We discuss how we can extend the DP-based methods to the reinforcement learning scenario where we only have access to data coming from the interaction of the agent with the environment.