Reinforcement learning refers to algorithms that are “goal-oriented.” They’re able to learn how to attain a complex objective, i.e. a goal by maximizing along a specific dimension over a number of iterations. For instance, maximizing the points obtained in a game over a number of moves. They can start from an initial blank slate, and under the right conditions they achieve extraordinary performance. These algorithms are penalized when they make the wrong decisions and rewarded when they make the right ones – this is how they engage the concept of reinforcement.