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AI glossary

Reinforcement Learning

Reinforcement learning is a machine learning method where a model learns by trial and error, guided by rewards it receives for good outcomes.

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In this guide

  1. 1What Reinforcement Learning means
  2. 2Why Reinforcement Learning matters

What Reinforcement Learning means

Reinforcement learning works much like training a pet. The model tries an action, sees whether it led to a good or bad result, and is "rewarded" for good ones. Over many attempts, it learns which choices tend to work best.

It is how AI learns to play games well: the system plays many rounds, scores higher for winning moves, and gradually develops a strong strategy. It is also used to make chatbots more helpful, based on human feedback.

Why Reinforcement Learning matters

Reinforcement learning helps shape how modern AI assistants behave, so the term is worth knowing. It explains how AI tools are tuned to be more helpful.

It is used to make chatbots more helpful and better aligned
It powers AI that learns strategy through practice
Knowing it helps you understand how AI tools are improved
It completes the picture of how AI models can learn

Frequently asked questions

Supervised learning gives the model correct answers to copy. Reinforcement learning gives only rewards or penalties for outcomes, so the model must discover good behavior through trial and error.

More AI terms

APIA defined way for two software systems to talk to each other and share data or features.Workflow AutomationSetting up a series of tasks to run automatically by connecting your apps and tools together.Retrieval-Augmented GenerationA technique that lets an AI look up relevant information first, then use it to give a more accurate answer.EmbeddingA way of turning text or other data into numbers that capture meaning, so AI can compare similarity.Vector DatabaseA database built to store embeddings and quickly find information by meaning rather than exact words.AI OrchestrationCoordinating multiple AI models, tools, and steps so they work together as one smooth system.

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