Traffic signal control plays a pivotal role in intelligent transportation systems, directly affecting urban mobility, congestion mitigation, and environmental sustainability. As traffic networks become more dynamic and complex, traditional strategies such as fixed-time and actuated control increasingly fall short in addressing real-time variability. In response, adaptive signal control—powered predominantly by reinforcement learning—has emerged as a promising data-driven solution for optimizing signal operations in evolving traffic environments. The current review presents a comprehensive analysis of high-impact reinforcement-learning-based traffic signal control methods, evaluating their contributions across numerous key dimensions: methodology type, multi-agent architectures, reward design, performance evaluation, baseline comparison, network scale, practical applicability, and simulation platforms. Through a systematic examination of the most influential studies, the review identifies dominant trends, unresolved challenges, and strategic directions for future research. The findings underscore the transformative potential of RL in enabling intelligent, responsive, and sustainable traffic management systems, marking a significant shift toward next-generation urban mobility solutions.

The integration of renewable energy systems into modern buildings is essential for enhancing energy efficiency, reducing carbon footprints, and advancing intelligent energy management. However, optimizing RES operations within building energy management systems introduces significant complexity, requiring advanced control strategies. One significant branch of modern control algorithms concerns reinforcement learning, a data-driven strategy capable of dynamically managing renewable energy sources and other energy subsystems under uncertainty and real-time constraints. The current review systematically examines RL-based control strategies applied in BEMS frameworks integrating RES technologies between 2015 and 2025, classifying them by algorithmic approach and evaluating the role of multi-agent and hybrid methods in improving real-time adaptability and occupant comfort. Following a thorough explanation of a rigorous selection process—which targeted the most impactful peer-reviewed publications from the last decade, the paper presents the mathematical concepts of RL and multi-agent RL, along with detailed summaries and summary tables of the integrated works to facilitate quick reference to key findings. For evaluation, the paper examines and outlines the different attributes in the field considering the following: methodologies of RL; agent types; value-action networks; reward functions; baseline control approaches; RES types; BEMS types; and building typologies. Grounded on the findings presented in the evaluation section, the paper offers a structured synthesis of emerging research trends and future directions, identifying the strengths and limitations of RL in energy management.