Reinforcement Learning (RL) is a paradigm where an Agent learns optimal behaviors by interacting with an Environment. Through trial and error, it discovers which actions maximize a cumulative reward signal, much like learning from experience. This iterative process allows the agent to make intelligent decisions over time.