Reinforcement Learning
Reinforcement Learning (RL) is a machine learning training paradigm where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards. The agent learns through trial-and-error feedback.
Frequently Asked Questions
What is the agent-environment loop in RL?▼
The agent observes the environment state, chooses an action, receives a reward or penalty, and transitions the environment to a new state.
What is the difference between supervised learning and RL?▼
Supervised learning trains on correct answers. RL trains on feedback (rewards or penalties) without direct labels.
Quick Facts
- CategoryModel Training
- Key ApplicationRobotics control, game-playing AI (AlphaGo), and autonomous system navigation
Coverage Trend12 Weeks
Related AI Terms
Reinforcement Learning Media Coverage & Intelligence
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