Reinforcement Learning (RL) is an area of machine learning where agents learn to make decisions by interacting with an environment to achieve maximum cumulative rewards. Key concepts in RL include states, actions, and rewards, which form the basis for learning optimal strategies. Q-Learning, a type of RL, involves learning a policy that tells an agent what action to take under what circumstances without requiring a model of the environment. Deep Q-Learning extends this by using neural networks to handle environments with large or continuous state spaces, enabling the model to approximate complex action-value functions efficiently.