Reinforcement Learning trains agents to maximize cumulative rewards through trial-and-error interactions with an environment. It models problems as Markov Decision Processes (MDPs) and uses algorithms like Q-Learning, Policy Gradients, and Actor-Critic. RL has led to breakthroughs in robotics and game-playing AI.