# What is Reinforcement Learning? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Model Training  
> **Canonical Citation:** [https://spidits.com/ai-glossary/reinforcement-learning](https://spidits.com/ai-glossary/reinforcement-learning)

## Definition
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.

## Architectural Mechanics & Deep Dive
Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards. Through trial-and-error interaction, the agent receives positive or negative feedback, updating its policy parameters to learn complex control and optimization strategies.

## Industry Applications & Real-World Use Cases
Robotics control, game-playing AI (AlphaGo), and autonomous system navigation

## 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.

## Related Terminology
- [supervised-learning](https://spidits.com/ai-glossary/supervised-learning)
- [unsupervised-learning](https://spidits.com/ai-glossary/unsupervised-learning)
- [agentic-ai](https://spidits.com/ai-glossary/agentic-ai)

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*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*