Supervised and unsupervised machine learning has shown great performance in finding mappings between probability distributions, as e.g. in classification problems or for artificial data generation. A more difficult class of problems is decision-making, e.g. controlling dynamical systems or building mathematical algorithms because the framework requires additional time-ordering. Reinforcement learning (RL) was successful in solving such problems, e.g. in finding strategies for games, optimizing algorithms for high-performance computing, and controlling magnetic fields for nuclear fusion reactors and particle accelerators. In this lecture, I will provide an introduction to the framework, with pedagogical examples, mathematical details, and applications in particle physics. In detail, I will cover: 1) Markov decision processes (MDPs) as the mathematical foundation of RL; 2) Solving small MPDs with tabular methods; 3) Solving large MDPs with policy gradient methods.