Energy Constraint

Energy constraint research focuses on optimizing resource utilization in systems where energy is a limiting factor, primarily aiming to improve efficiency and performance while adhering to energy budgets. Current research employs various machine learning approaches, including reinforcement learning (e.g., Deep Q-learning, Multi-Agent Reinforcement Learning), and heuristic search algorithms to address energy limitations in diverse applications such as federated learning, robotic path planning, and AI model training. These efforts are significant because they enable the development of more sustainable and practical AI systems and robotic applications, particularly in resource-constrained environments like IoT devices and mobile robots.