Reinforcement learning (RL) is a stand-alone branch of machine learning, neither supervised nor unsupervised where an algorithm gradually learns by interacting with an environment. RL makes decisions based on its past experience about which actions can bring it closer to a stated goal. By receiving rewards for correct actions and penalties for wrong ones, the algorithm finds out the optimal strategy to maximize its performance.