Reinforcement Learning
is a machine learning approach in which an agent learns to make decisions by interacting with an environment and receiving rewards or penalties based on its actions.
What is Reinforcement Learning?
In reinforcement learning, an agent observes the current state, selects an action, and receives feedback from the environment. Over repeated interactions, it learns a policy that aims to maximize cumulative reward. Unlike supervised learning, it does not require a labeled example for every possible action.
Why is Reinforcement Learning Important?
Reinforcement learning is useful for problems where decisions occur sequentially and actions affect future outcomes. It allows models to learn strategies through interaction and feedback, making it suitable for dynamic environments and complex decision-making tasks.
Common use cases
Reinforcement Learning is commonly used in robotics, game playing, autonomous systems, resource optimization, recommendation systems, and AI agents.