Reinforcement learning (RL) is an artificial intelligence approach that enables decision makers to learn and take appropriate actions in a dynamic and unpredictable operating environment. Compared to other artificial intelligence approaches, including supervised and unsupervised learning, RL has a distinguishing aspect in which it learns through interaction with the environment. By receiving rewards (or penalties) from the environment, a decision maker can evaluate the appropriateness of its selected action under a particular environment, and so a teacher or a critic is not required to be present to tell whether an action is appropriate or inappropriate. The fact that RL has outperformed human experts in various computer games, such as the Atari games, has drawn wide interest in exploring and exploiting the application of RL to solve a diverse range of problems and enhance next-generation technologies. This talk covers the fundamental aspects of RL, including the Markov decision process problem, algorithms, state-of-the-art models and algorithms, simulation, and open issues. Ultimately, it guides participants in exploring the use of RL to provide solutions to problems and issues at hand.