RW, or Reinforcement Learning, is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards. The agent receives feedback in the form of rewards or penalties based on its actions, allowing it to learn optimal strategies over time. In RW, the agent explores different actions and learns from the outcomes, gradually improving its performance. This approach is widely used in various applications, including robotics, game playing, and autonomous systems, where the ability to adapt and learn from experience is crucial for success.