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.

Chatbots serve as a valuable tool for enhancing customer interactions. The traditional debt collection process is time-consuming and requires significant h

The increase of connected devices in the Internet of Things (IoT) and Industrial Internet of Things (IIoT) has led to a surge in the number of potential attacks. Traditional methods of intrusion detection can hardly be effective against zero-day attacks. AI-based solutions like Deep Learning (DL) and Machine Learning (ML) provide adaptable and intelligent detection of threats. The paper discusses the present implementation of AI-based IDS systems and the problems they still face in the real world. Deep learning methods, such as LSTMs (Long Short-Term Memory), CNNs (Convolutional Neural Networks), DNNs (Deep Neural Networks), and Deep Belief Networks, can achieve an accuracy for recognition of 99%. It discusses the various kinds of attacks and how it affects the real world applications. On the other hand, these systems have not become mainstream yet as they produce many false alarms and require heavy experimentation. According to this survey, the next step of innovation in IDS should be toward the creation of lightweight, interpretable models to ensure reliability and scalability.