[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122902-en":3,"doc-seo-122902-105":29,"detail-sidebar-cat-0-en-105":89},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":11},122902,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Why Reinforcement Learning?","The article introduces reinforcement learning (RL) as an active learning paradigm in which an AI-driven agent learns through trial and error in an interactive environment, using feedback from its own actions and experiences. It explains the reward-and-punishment mechanism that trains the agent to maximize correct choices while minimizing incorrect ones. The discussion highlights RL’s value for dynamic, non-stable, and online scenarios, and outlines major application areas such as robotics, gaming, and decision-making tasks.","Why Reinforcement Learning?  \nAydin, M. E., Durgut, R. & Rakib, A.  \nPublished PDF deposited in Coventry University’s Repository  \nOriginal citation:  \nAydin, ME, Durgut, R & Rakib, A 2024, 'Why Reinforcement Learning?', Algorithms, vol. 17, no. 6, 269. [https://doi.org/10.3390/a17060269](https://doi.org/10.3390/a17060269)  \n[DOI 10.3390/a17060269](DOI 10.3390/a17060269)[ ](DOI 10.3390/a17060269)[ESSN 1999-4893](ESSN 1999-4893)  \nPublisher: MDPI  \n© 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/](https://creativecommons.org/licenses/by/4.0/)).  \n algorithms  \nEditorial  \nWhy Reinforcement Learning?  \nMehmet Emin Aydin 1, *, Rafet Durgut 2 and Abdur Rakib 3  \nCitation: Aydin, M.E.; Durgut, R.; Rakib, A. Why Reinforcement Learning? Algorithms 2024, 17, 269 . [https://doi.org/10.3390/a17060269](https://doi.org/10.3390/a17060269)  \nReceived: 22 May 2024  \nAccepted: 17 June 2024  \nPublished: 20 June 2024  \nCopyright: © 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 School of Computer Science and Creative Technologies, University of the West of England, Bristol BS16 1QY, UK  \n2 Crystalloids BV, 3013 AK Rotterdam, The Netherlands; [durgutrafet@gmail.com](durgutrafet@gmail.com)  \n3 Centre for Future Transport and Cities, Coventry University, Coventry CV1 5FB, UK; [ad9812@coventry.ac.uk](ad9812@coventry.ac.uk)  \n* [Correspondence: mehmet.aydin@uwe.ac.uk](Correspondence: mehmet.aydin@uwe.ac.uk)  \nThe 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 advancementsin 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. Reinforcement learning is a type of active learning whereby a trainee agent learns by performing desired tasks. This is very useful, especially when labelled data are unavailable or difficult to obtain beforehand but can only be accessed while running the system. Moreover, it is particularly useful for dynamic and non-stable problems, as well as online and ever-changing cases. Robotic and gaming applications are two well-known areas of application, and researchers are increasingly focusing on numerous emerging use cases [1] .  \nReinforcement learning (RL), a modern machine learning paradigm, enables an AIdriven system (known as an agent) to learn in an interactive environment via trial and error using feedback from its own actions and experiences [2] . The basic idea behind RL is to train the agent by a reward-and-punishment mechanism [3] whereby the agent receives rewards for performing correct actions and is punished for incorrect ones. Through this process, the agent aims to maximize appropriate choices while minimizing incorrect ones. This has paved the way for allowing learning agents to adapt to changing circumstances in order to fulfil a specific goal, as, based on the feedback responses, the agent assesse","cbCaima0JfRwUv4Z","https://ap.wps.com/l/cbCaima0JfRwUv4Z","pdf",579730,1,3,"English","en",105,"# Introduction\n## Core idea and training mechanism\n## Applications and research directions\n## Special Issue overview and contributions","[{\"question\":\"What is reinforcement learning in the article’s definition?\",\"answer\":\"Reinforcement learning is presented as an active learning approach where an agent learns by performing tasks and receiving feedback from its own actions and experiences.\"},{\"question\":\"How does the reward-and-punishment mechanism work?\",\"answer\":\"The agent receives rewards for correct actions and is punished for incorrect ones, aiming to maximize appropriate choices and minimize wrong decisions over time.\"},{\"question\":\"Why is RL especially useful for real-world scenarios?\",\"answer\":\"RL is emphasized as particularly effective for dynamic and non-stable problems, including online and ever-changing cases where labeled data may be unavailable or hard to obtain beforehand.\"}]","Why Reinforcement Learning? 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