Reinforcement Learning

Reinforcement Learning

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. Instead of learning from labeled data, the agent learns by trial and error, receiving rewards for good actions and penalties for bad ones.

How it works:

• The agent takes an action in the environment.

• The environment responds by giving feedback (a reward or penalty).

• The agent uses this feedback to improve its future actions, aiming to maximize cumulative rewards over time.

Examples:

• Teaching a robot to walk by rewarding balanced movements.

• Training AI to play games like chess or Go by learning winning strategies through practice.

• Optimizing recommendation systems by learning which suggestions users interact with most.

Reinforcement Learning is powerful for problems where decisions need to be made sequentially and where the best action depends on the current situation.