Reinforcement Learning
AI/ML Fundamentals
Learning through trial and error with rewards/penalties
What is Reinforcement Learning?
An agent learns by interacting with an environment, receiving rewards for good actions and penalties for bad ones. Like training a dog with treats.
Real-World Examples
- •Game-playing AI (AlphaGo)
- •Robot control
- •Trading algorithms
- •Recommendation optimization
When to Use This
For sequential decision-making problems where feedback comes after actions
Related Terms
Learn more about concepts related to Reinforcement Learning