Reinforcement Learning
Definition
Reinforcement Learning (RL) is a branch of machine learning where an agent learns by interacting with its environment, receiving rewards for correct actions and penalties for incorrect ones, aiming to maximize its total reward.
Explain Like I'm 5
Imagine you're playing a video game for the first time. You don’t know the rules, so you try different things. Sometimes you win points, sometimes you lose them. Over time, you learn which actions help you score more points. That’s how reinforcement learning works—by trial and error!
Visual Aid
Digging Deeper
Reinforcement Learning (RL) is a branch of machine learning where an agent learns by interacting with its environment, receiving rewards for correct actions and penalties for incorrect ones, aiming at maximizing its total reward.
It involves exploring the environment initially without any knowledge and gradually improving its behavior based on the feedback received through rewards or penalties. This learning process is based on the concept of "reward hypothesis". According to this hypothesis, all goals can be described by the maximization of expected cumulative reward.
In RL, an agent makes decisions by following a policy - a map from state to action that tells it what action to take under what circumstances.
Applications
Reinforcement Learning is widely used in:
- Robotics – Training robots to interact with environments.
- Gaming – AI agents mastering games like chess and poker.
- Recommendation Systems – Learning user preferences over time.
- Healthcare – Designing personalized treatments based on patient responses.
- Finance – Algorithmic trading strategies.
- Energy Efficiency – Optimizing power usage in smart grids.
Learn More
- Wikipedia: Reinforcement Learning
- Beginner’s Guide to Reinforcement Learning (YouTube)
- Introduction to Reinforcement Learning (Coursera)