Reinforcement Learning

Reinforcement Learning is a computational approach where an agent learns to make decisions by performing actions in an environment to achieve maximum cumulative reward. The agent receives feedback in the form of rewards or penalties and adjusts its strategy accordingly. Through trial and error, the agent learns to maximize its cumulative reward by developing optimal behavioral strategies (policies), making it effective for tasks like game playing, robotics, and resource management.

Autonomous driving (navigating roads) game playing AI (e.g. AlphaGo) robotics (manipulating objects) resource management (optimizing operations) personalized recommendations.