Definition
Reinforcement Learning (RL) is a branch of machine learning focused on how intelligent agents ought to take actions in an environment to maximize the notion of cumulative reward. Unlike supervised learning, RL does not need labeled input/output pairs but instead focuses on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). The agent learns a policy that maps states to actions, improving over time through trial and error.
Summary
Reinforcement Learning is a type of machine learning where agents learn to make decisions by interacting with an environment to maximize rewards.
Key Concepts
- Agent
- Environment
- Policy
- Reward Function
- State Space
Use Cases
- Autonomous driving
- Game AI (e.g., AlphaGo)
- Recommendation systems