Description

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties for its actions, and its goal is to learn a policy that maximizes the cumulative reward over time. RL is inspired by behavioral psychology and is particularly well-suited for problems involving sequential decision-making under uncertainty.

Examples

• 🎲 AlphaGo
• 🤖 Robotic control
• 🎮 Game AI
• 🚗 Autonomous vehicles

Applications