Reinforcement learning (RL) is a branch of machine learning focused on how agents should take actions in an environment to maximize a cumulative reward. In RL, an agent interacts with its environment by performing actions and receiving feedback in the form of rewards or penalties. The goal is to learn a policy that maps states of the environment to the best actions to take, optimizing long-term rewards. RL has been successfully applied in various fields, including robotics, game playing, and autonomous systems, as it enables machines to learn from trial and error, adapting to complex and dynamic situations.