Reinforcement Learning (RL) is a type of machine learning paradigm where an agent learns to make decisions by interacting with an environment. In RL, the agent takes actions in the environment, and based on those actions, it receives feedback in the form of rewards or penalties. The goal of the agent is to learn a policy or strategy that maximizes the cumulative reward over time. Unlike supervised learning, where the algorithm is trained on labeled input-output pairs, and unsupervised learning, where the algorithm discovers patterns without explicit guidance, reinforcement learning involves learning from trial and error in a dynamic environment. Key components of reinforcement learning include the agent, the environment, actions, rewards, and the policy, which guides the agent's decision-making. RL has been successfully applied in various domains, including robotics, game playing, autonomous systems, and optimization problems.

Note: This concise definition provides an overview of Reinforcement Learning. For further information, a more in-depth search on Google is recommended.