reinforcement learning (rl) is an area of machine learning where an agent learns to behave in an environment by performing actions and seeing the results. in reinforcement learning, an agent makes decisions by following a policy - a strategy that dictates the decision-making process. the agent receives rewards or penalties in the form of positive or negative reinforcement, hence the name. this feedback helps the algorithm adjust its course of action until the best strategy is found. applications of reinforcement learning include gaming (where rl algorithms have achieved superhuman performance), robotics (for teaching machines to perform complex maneuvers like backflips), and in optimization problems.