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.

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A Systematic Study on Reinforcement Learning Based Applications

Real-time energy scheduling for home energy management systems with an energy storage system and electric vehicle based on a supervised-learning-based strategy

Fuzzy Q-learning-based approach for real-time energy management of home microgrids using cooperative multi-agent system

With the advancements in renewable energy and energy storage technologies, the energy hubs (EH) have been emerging in recent years. The scheduling of EH is a challenging task due to the need to incorporate uncertainties at energy supply and load side. The existing model-based optimization approaches proposed for the above purpose have limitations in terms of solution accuracy and computational efficiency, which makes hinders their applications. This paper proposes a model-free, safe deep reinforcement learning (DRL) approach, using primal-dual optimization and imitation learning, for optimal scheduling of an EH that includes a tri-generative advanced adiabatic compressed air energy storage (AA-CAES). First, the operation of an AA-CAES under off-design conditions is modeled and linearized using a mixed-integer linear programming (MILP). Then, a safe DRL approach is proposed with training and testing steps considering a case study. The performance of the proposed approach in reducing operational cost and satisfying constraints is compared to the state-of-the-art DRL algorithms as well as a deterministic MILP approach. In addition, the generalization of the proposed approach is examined on a test-set. Finally, the effect of off-design conditions of a tri-generative AA-CAES on the optimal dispatch strategy is investigated. The results indicate that the proposed approach can effectively reduce the operational cost and satisfy the operational constraints. 

Optimal dispatch of an energy hub with compressed air energy storage: A safe reinforcement learning approach

This article presents a home energy management system (HEMS) based on the proposed adaptive artificial bee colony (AABC) optimization method, of managing a residential microgrid connected to the main grid. The studied microgrid consists of a photovoltaic system (PVS), a battery storage system (BSS), and electricity loads. The proposed AABC-based HEMS optimizes power flow ratio among PVS, BSS, and the main grid with respect to the load demand and electricity rate in order to minimize electricity consumption cost. Three dc-dc converters are actively controlled with an active dual-loop control architecture to dominate the power distribution among multi-power sources. The performances of the proposed optimal power flows control (PFC) under different electricity load levels and PVS generation conditions are examined. In the PFC experiments, the designed HEMS is implemented on a digital signal processor. The experimental results demonstrate the effectiveness and superiority of the proposed AABC-based HEMS compared with the rule-based control strategy and the equivalent consumption minimization strategy in terms of electricity consumption cost. 

Optimal Power Flows Control for Home Energy Management With Renewable Energy and Energy Storage Systems

A safe reinforcement learning approach for multi-energy management of smart home

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A safe reinforcement learning approach for multi-energy management of smart home