Reinforcement learning (RL) addresses sequential decision-making problems and stochastic control and is strongly connected to dynamic programming and Markov decision processes. In the last decades, it has gained importance and has become a major field of study in machine learning and artificial intelligence. Researchers from various scientific fields that reach from cognitive sciences, neurology, and psychology to computer science, physics, and mathematics have developed algorithms and techniques with impressive applications and mathematical foundations. Reinforcement learning is based on the simple idea of learning by trial and error while interacting with an environment. At each step, the agent acts and receives a reward depending on the starting state, the action, and the environment. The agent learns to choose actions that maximize the sum of all rewards in the long run. The resulting choice of action for each state is called a policy. Finding optimal policies is the primary objective of reinforcement learning.