A Systematic Study on Reinforcement Learning Based Applications

Keerthana Sivamayil, Elakkiya Rajasekar, Belqasem Aljafari, Srete Nikolovski (), Subramaniyaswamy Vairavasundaram () and Indragandhi Vairavasundaram
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Keerthana Sivamayil: School of Computing, SASTRA Deemed University, Thanjavur 613401, India
Elakkiya Rajasekar: School of Computing, SASTRA Deemed University, Thanjavur 613401, India
Belqasem Aljafari: Department of Electrical Engineering, Najran University, Najran 11001, Saudi Arabia
Srete Nikolovski: Power Engineering Department, Faculty of Electrical Engineering, Computer Science and Information Technology, J. J. Strossmayer University of Osijek, K. Trpimira 2B, HR-31000 Osijek, Croatia
Subramaniyaswamy Vairavasundaram: School of Computing, SASTRA Deemed University, Thanjavur 613401, India
Indragandhi Vairavasundaram: School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, India

Energies, 2023, vol. 16, issue 3, 1-23

Abstract: We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet of things security, recommendation systems, finance, and energy management. The optimization of energy use is critical in today’s environment. We mainly focus on the RL application for energy management. Traditional rule-based systems have a set of predefined rules. As a result, they may become rigid and unable to adjust to changing situations or unforeseen events. RL can overcome these drawbacks. RL learns by exploring the environment randomly and based on experience, it continues to expand its knowledge. Many researchers are working on RL-based energy management systems (EMS). RL is utilized in energy applications such as optimizing energy use in smart buildings, hybrid automobiles, smart grids, and managing renewable energy resources. RL-based energy management in renewable energy contributes to achieving net zero carbon emissions and a sustainable environment. In the context of energy management technology, RL can be utilized to optimize the regulation of energy systems, such as building heating, ventilation, and air conditioning (HVAC) systems, to reduce energy consumption while maintaining a comfortable atmosphere. EMS can be accomplished by teaching an RL agent to make judgments based on sensor data, such as temperature and occupancy, to modify the HVAC system settings. RL has proven beneficial in lowering energy usage in buildings and is an active research area in smart buildings. RL can be used to optimize energy management in hybrid electric vehicles (HEVs) by learning an optimal control policy to maximize battery life and fuel efficiency. RL has acquired a remarkable position in robotics, automated cars, and gaming applications. The majority of security-related applications operate in a simulated environment. The RL-based recommender systems provide good suggestions accuracy and diversity. This article assists the novice in comprehending the foundations of reinforcement learning and its applications.

Keywords: machine learning; reinforcement learning; deep reinforcement learning; Markov decision process; contextual bandits; inverse reinforcement learning; multi-agent RL; energy management system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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