Why Reinforcement Learning?

Mehmet Emin Aydin, Rafet Durgut, Abdur Rakib

Research output: Contribution to journalEditorialpeer-review

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Abstract

The term Artificial Intelligence (AI) has come to be one of the most frequently expressed keywords around the globe. Machine learning (ML) continues to gain popularity in the provision of solutions to both industrial and everyday problems, and advancements in infrastructure computing technologies have driven a surge of interest in AI, ML, and particularly large language models (LLMs). This involves huge data stocks and bulky data processing. However, many real-world problems lack the necessary existing data for modelling and model training. Furthermore, numerous dynamic problems do not retain data for later use due to constantly evolving circumstances, resulting in significant challenges in identifying or uncovering patterns (domain knowledge) within such dynamic structures and situations. These problems remain as significant and outstanding challenges.
Original languageEnglish
Article number269
Number of pages2
JournalAlgorithms
Volume17
Issue number6
DOIs
Publication statusPublished - 20 Jun 2024

Bibliographical note

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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