# Reinforcement Learning

> Source: https://sukruyusufkaya.com/en/glossary/pekistirmeli-ogrenme
> Updated: 2026-05-13T21:12:30.334Z
> Type: glossary
> Category: yapay-zeka-temelleri
**TLDR:** A paradigm in which an agent learns a long-term behavior policy through rewards and penalties by interacting with its environment.

<p>Reinforcement learning is a powerful paradigm based on an agent learning which behaviors lead to better long-term outcomes through interaction with an environment. The system does not memorize single correct answers; instead, it observes the consequences of its choices, receives rewards or penalties, and gradually improves its strategy. This framework appears in game-playing agents, robotics, dynamic resource allocation, and many sequential decision scenarios. What makes reinforcement learning especially interesting is that it produces behavior learning, not just prediction. Its challenge is that poorly designed rewards can lead to systems that are technically “successful” while behaving in undesirable ways in practice.</p>